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Nicolas Chopin

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Alexander Buchholz & Nicolas CHOPIN, 2017. "Improving approximate Bayesian computation via quasi Monte Carlo," Working Papers 2017-37, Center for Research in Economics and Statistics.

    Cited by:

    1. Jean-Jacques Forneron, 2019. "A Scrambled Method of Moments," Papers 1911.09128, arXiv.org.

  2. Nicolas Chopin & Sumeetpal S. Singh, 2013. "On the Particle Gibbs Sampler," Working Papers 2013-41, Center for Research in Economics and Statistics.

    Cited by:

    1. Bournakis, Ioannis & Tsionas, Mike G., 2023. "A Non-Parametric Estimation of Productivity with Idiosyncratic and Aggregate Shocks: The Role of Research and Development (R&D) and Corporate Tax," MPRA Paper 118100, University Library of Munich, Germany.
    2. Tsionas, Mike G. & Mallick, Sushanta K., 2019. "A Bayesian semiparametric approach to stochastic frontiers and productivity," European Journal of Operational Research, Elsevier, vol. 274(1), pages 391-402.
    3. Creal, Drew D. & Tsay, Ruey S., 2015. "High dimensional dynamic stochastic copula models," Journal of Econometrics, Elsevier, vol. 189(2), pages 335-345.
    4. Axel Finke & Adam Johansen & Dario Spanò, 2014. "Static-parameter estimation in piecewise deterministic processes using particle Gibbs samplers," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 577-609, June.
    5. Mike G. Tsionas & Nicholas Apergis, 2023. "Another look at contagion across United States and European financial markets: Evidence from the credit default swaps markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 1137-1155, January.
    6. Emmanuel C. Mamatzakis & Steven Ongena & Mike G. Tsionas, 2023. "The response of household debt to COVID-19 using a neural networks VAR in OECD," Empirical Economics, Springer, vol. 65(1), pages 65-91, July.
    7. Tsionas, Mike G., 2020. "On a model of environmental performance and technology gaps," European Journal of Operational Research, Elsevier, vol. 285(3), pages 1141-1152.
    8. Patrick Leung & Catherine S. Forbes & Gael M. Martin & Brendan McCabe, 2016. "Data-driven particle Filters for particle Markov Chain Monte Carlo," Monash Econometrics and Business Statistics Working Papers 17/16, Monash University, Department of Econometrics and Business Statistics.
    9. Mengheng Li & Marcel Scharth, 2022. "Leverage, Asymmetry, and Heavy Tails in the High-Dimensional Factor Stochastic Volatility Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 285-301, January.
    10. Tsionas, Mike G., 2022. "Convex non-parametric least squares, causal structures and productivity," European Journal of Operational Research, Elsevier, vol. 303(1), pages 370-387.
    11. Mike G. Tsionas & Subal C. Kumbhakar, 2023. "Productivity and Performance: A GMM approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(2), pages 331-344, April.

  3. Nicolas CHOPIN & Pierre DEL MORAL & Sylvain RUBENTHALER, 2010. "Stability of Feynman-Kac Formulae with Path-dependent Potentials," Working Papers 2010-03, Center for Research in Economics and Statistics.

    Cited by:

    1. Whiteley, Nick & Kantas, Nikolas & Jasra, Ajay, 2012. "Linear variance bounds for particle approximations of time-homogeneous Feynman–Kac formulae," Stochastic Processes and their Applications, Elsevier, vol. 122(4), pages 1840-1865.

  4. Nicolas Chopin & Tony Lelievre & Gabriel Stoltz, 2010. "Free Energy Methods for Efficient Exploration of Mixture Posterior Densities," Working Papers 2010-33, Center for Research in Economics and Statistics.

    Cited by:

    1. Nicolas Chopin & Pierre Jacob, 2010. "Free Energy Sequential Monte Carlo Application to Mixture Modelling," Working Papers 2010-34, Center for Research in Economics and Statistics.

  5. Nicolas Chopin & Alessandra Iacobucci & Jean-Michel Marin & Kerrie L. Mengersen & Christian P. Robert & Robin Ryder & Christian Schafer, 2010. "On Particle Learning," Working Papers 2010-22, Center for Research in Economics and Statistics.

    Cited by:

    1. Karol Gellert & Erik Schlögl, 2021. "Parameter Learning and Change Detection Using a Particle Filter with Accelerated Adaptation," Risks, MDPI, vol. 9(12), pages 1-18, December.
    2. Chen, Ji & Yang, Xinglin & Liu, Xiliang, 2022. "Learning, disagreement and inflation forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    3. Audrone Virbickaite & Hedibert F. Lopes & Maria Concepción Ausín & Pedro Galeano, 2018. "Particle Learning for Bayesian Semi-Parametric Stochastic Volatility Model," DEA Working Papers 88, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    4. Audrone Virbickaite & Hedibert F. Lopes, 2018. "Bayesian Semi-Parametric Markov Switching Stochastic Volatility Model," DEA Working Papers 89, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    5. Bhattacharya, Arnab & Wilson, Simon P., 2018. "Sequential Bayesian inference for static parameters in dynamic state space models," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 187-203.

  6. Judith Rousseau & Nicolas Chopin & Brunero Liseo, 2010. "Bayesian Nonparametric Estimation of the Spectral Density of a Long or Intermediate Memory Gaussian Process," Working Papers 2010-38, Center for Research in Economics and Statistics.

    Cited by:

    1. Julyan Arbel & Ghislaine Gayraud & Judith Rousseau, 2013. "Bayesian Optimal Adaptive Estimation Using a Sieve prior," Working Papers 2013-19, Center for Research in Economics and Statistics.

  7. Nicolas Chopin & Pierre Jacob, 2010. "Free Energy Sequential Monte Carlo Application to Mixture Modelling," Working Papers 2010-34, Center for Research in Economics and Statistics.

    Cited by:

    1. Drovandi, Christopher C. & Pettitt, Anthony N. & Henderson, Robert D. & McCombe, Pamela A., 2014. "Marginal reversible jump Markov chain Monte Carlo with application to motor unit number estimation," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 128-146.
    2. Garland Durham & John Geweke, 2013. "Adaptive Sequential Posterior Simulators for Massively Parallel Computing Environments," Working Paper Series 9, Economics Discipline Group, UTS Business School, University of Technology, Sydney.

  8. Nicolas Chopin, 2002. "Central Limit Theorem for Sequential Monte Carlo Methods and its Applications to Bayesian Inference," Working Papers 2002-44, Center for Research in Economics and Statistics.

    Cited by:

    1. Jasra, Ajay & Doucet, Arnaud, 2008. "Stability of sequential Monte Carlo samplers via the Foster-Lyapunov condition," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 3062-3069, December.
    2. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2013. "Parallel Sequential Monte Carlo for Efficient Density Combination: The Deco Matlab Toolbox," CREATES Research Papers 2013-09, Department of Economics and Business Economics, Aarhus University.
    3. Gareth W. Peters & Efstathios Panayi & Francois Septier, 2015. "SMC-ABC methods for the estimation of stochastic simulation models of the limit order book," Papers 1504.05806, arXiv.org.
    4. Laurent-Emmanuel Calvet & Veronika Czellar, 2011. "State-Observation Sampling and the Econometrics of Learning Models," Working Papers hal-00625500, HAL.
    5. Saikat Saha, 2015. "Noise Robust Online Inference for Linear Dynamic Systems," Papers 1504.05723, arXiv.org.
    6. Johansen, Adam M. & Doucet, Arnaud, 2008. "A note on auxiliary particle filters," Statistics & Probability Letters, Elsevier, vol. 78(12), pages 1498-1504, September.
    7. Jie Xiong & Yong Zeng, 2011. "A branching particle approximation to a filtering micromovement model of asset price," Statistical Inference for Stochastic Processes, Springer, vol. 14(2), pages 111-140, May.
    8. John Geweke, 2016. "Sequentially Adaptive Bayesian Learning for a Nonlinear Model of the Secular and Cyclical Behavior of US Real GDP," Econometrics, MDPI, vol. 4(1), pages 1-23, March.
    9. Marco Del Negro & Raiden B. Hasegawa & Frank Schorfheide, 2014. "Dynamic Prediction Pools: An Investigation of Financial Frictions and Forecasting Performance," PIER Working Paper Archive 14-034, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    10. Creal, D., 2009. "A survey of sequential Monte Carlo methods for economics and finance," Serie Research Memoranda 0018, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    11. Qian, Hang, 2015. "Inequality Constrained State Space Models," MPRA Paper 66447, University Library of Munich, Germany.
    12. Markku Lanne & Jani Luoto, 2015. "Estimation of DSGE Models under Diffuse Priors and Data-Driven Identification Constraints," CREATES Research Papers 2015-37, Department of Economics and Business Economics, Aarhus University.
    13. Targino, Rodrigo S. & Peters, Gareth W. & Shevchenko, Pavel V., 2015. "Sequential Monte Carlo Samplers for capital allocation under copula-dependent risk models," Insurance: Mathematics and Economics, Elsevier, vol. 61(C), pages 206-226.
    14. Garland Durham & John Geweke, 2013. "Adaptive Sequential Posterior Simulators for Massively Parallel Computing Environments," Working Paper Series 9, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    15. Nicolas Chopin, 2007. "Dynamic Detection of Change Points in Long Time Series," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(2), pages 349-366, June.
    16. Daniel F. Waggoner & Hongwei Wu & Tao Zha, 2014. "The Dynamic Striated Metropolis-Hastings Sampler for High-Dimensional Models," FRB Atlanta Working Paper 2014-21, Federal Reserve Bank of Atlanta.

  9. Nicolas Chopin, 2001. "Sequential Inference and State Number Determination for Discrete State-Space Models through Particle Filtering," Working Papers 2001-34, Center for Research in Economics and Statistics.

    Cited by:

    1. Gianni Amisano & Roberto Casarin, 2008. "Particle Filters for Markov-Switching Stochastic-Correlation Models," Working Papers 0814, University of Brescia, Department of Economics.
    2. Chopin, Nicolas & Pelgrin, Florian, 2004. "Bayesian inference and state number determination for hidden Markov models: an application to the information content of the yield curve about inflation," Journal of Econometrics, Elsevier, vol. 123(2), pages 327-344, December.
    3. Benoit Bellone, 2004. "Une lecture probabiliste du cycle d’affaires américain," Econometrics 0407002, University Library of Munich, Germany, revised 28 Mar 2005.
    4. Nicolas Chopin, 2002. "Central Limit Theorem for Sequential Monte Carlo Methods and its Applications to Bayesian Inference," Working Papers 2002-44, Center for Research in Economics and Statistics.
    5. Benoit Bellone, 2004. "MSVARlib: a new Gauss library to estimate multivariate Hidden Markov Models," Econometrics 0406004, University Library of Munich, Germany.

  10. Nicolas Chopin, 2000. "A Sequential Particle Filter Method for Static Models," Working Papers 2000-45, Center for Research in Economics and Statistics.

    Cited by:

    1. Salima El Kolei, 2013. "Parametric estimation of hidden stochastic model by contrast minimization and deconvolution," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(8), pages 1031-1081, November.
    2. Arnaud Dufays, 2014. "On the conjugacy of off-line and on-line Sequential Monte Carlo Samplers," Working Paper Research 263, National Bank of Belgium.
    3. Xiaohong Chen & Timothy M. Christensen & Elie Tamer, 2017. "Monte Carlo confidence sets for identified sets," CeMMAP working papers CWP43/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Mariolis Theodore & Konstantakis Konstantinos N. & Michaelides Panayotis G. & Tsionas Efthymios G., 2019. "A non-linear Keynesian Goodwin-type endogenous model of the cycle: Bayesian evidence for the USA," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(1), pages 1-16, February.
    5. Håvard Rue & Ingelin Steinsland & Sveinung Erland, 2004. "Approximating hidden Gaussian Markov random fields," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 877-892, November.
    6. Edward P. Herbst & Frank Schorfheide, 2012. "Sequential Monte Carlo sampling for DSGE models," Working Papers 12-27, Federal Reserve Bank of Philadelphia.
    7. Shi, Minghui & Dunson, David B., 2011. "Bayesian variable selection via particle stochastic search," Statistics & Probability Letters, Elsevier, vol. 81(2), pages 283-291, February.
    8. Michael Cai & Marco Del Negro & Edward P. Herbst & Ethan Matlin & Reca Sarfati & Frank Schorfheide, 2019. "Online Estimation of DSGE Models," Liberty Street Economics 20190821, Federal Reserve Bank of New York.
    9. Trong‐Nghia Nguyen & Minh‐Ngoc Tran & Robert Kohn, 2022. "Recurrent conditional heteroskedasticity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1031-1054, August.
    10. Chopin, Nicolas & Pelgrin, Florian, 2004. "Bayesian inference and state number determination for hidden Markov models: an application to the information content of the yield curve about inflation," Journal of Econometrics, Elsevier, vol. 123(2), pages 327-344, December.
    11. Nathan Cunningham & Jim E. Griffin & David L. Wild, 2020. "ParticleMDI: particle Monte Carlo methods for the cluster analysis of multiple datasets with applications to cancer subtype identification," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(2), pages 463-484, June.
    12. Mevin B. Hooten & Michael R. Schwob & Devin S. Johnson & Jacob S. Ivan, 2023. "Multistage hierarchical capture–recapture models," Environmetrics, John Wiley & Sons, Ltd., vol. 34(6), September.
    13. Mike G. Tsionas, 2017. "“When, Where, and How” of Efficiency Estimation: Improved Procedures for Stochastic Frontier Modeling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 948-965, July.
    14. Hoogerheide, Lennart & Opschoor, Anne & van Dijk, Herman K., 2012. "A class of adaptive importance sampling weighted EM algorithms for efficient and robust posterior and predictive simulation," Journal of Econometrics, Elsevier, vol. 171(2), pages 101-120.
    15. C. C. Drovandi & A. N. Pettitt, 2011. "Estimation of Parameters for Macroparasite Population Evolution Using Approximate Bayesian Computation," Biometrics, The International Biometric Society, vol. 67(1), pages 225-233, March.
    16. Gunawan, David & Dang, Khue-Dung & Quiroz, Matias & Kohn, Robert & Tran, Minh-Ngoc, 2019. "Subsampling Sequential Monte Carlo for Static Bayesian Models," Working Paper Series 371, Sveriges Riksbank (Central Bank of Sweden).
    17. Hirokuni Iiboshi & Mototsugu Shintani & Kozo Ueda, 2018. "Estimating a Nonlinear New Keynesian Model with a Zero Lower Bound for Japan," Working Papers e120, Tokyo Center for Economic Research.
    18. Rubio-Ramírez, Juan Francisco & Schorfheide, Frank & Fernández-Villaverde, Jesús, 2015. "Solution and Estimation Methods for DSGE Models," CEPR Discussion Papers 11032, C.E.P.R. Discussion Papers.
    19. Arnaud Dufays, 2016. "Evolutionary Sequential Monte Carlo Samplers for Change-Point Models," Econometrics, MDPI, vol. 4(1), pages 1-33, March.
    20. Joel A Paulson & Marc Martin-Casas & Ali Mesbah, 2019. "Fast uncertainty quantification for dynamic flux balance analysis using non-smooth polynomial chaos expansions," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-35, August.
    21. Jeong Eun Lee & Christian Robert, 2013. "Imortance Sampling Schemes for Evidence Approximation in Mixture Models," Working Papers 2013-42, Center for Research in Economics and Statistics.
    22. Ettmeier, Stephanie & Kriwoluzky, Alexander, 2019. "Active, or passive? Revisiting the role of fiscal policy in the Great Inflation," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203609, Verein für Socialpolitik / German Economic Association.
    23. Wan, Runqing & Fulop, Andras & Li, Junye, 2022. "Real-time Bayesian learning and bond return predictability," Journal of Econometrics, Elsevier, vol. 230(1), pages 114-130.
    24. Andras Fulop & Junye Li & Jun Yu, 2012. "Bayesian Learning of Impacts of Self-Exciting Jumps in Returns and Volatility," Working Papers 03-2012, Singapore Management University, School of Economics.
    25. Nicolas Chopin & Mathieu Gerber, 2017. "Sequential quasi-Monte Carlo: Introduction for Non-Experts, Dimension Reduction, Application to Partly Observed Diffusion Processes," Working Papers 2017-35, Center for Research in Economics and Statistics.
    26. Axel Finke & Adam Johansen & Dario Spanò, 2014. "Static-parameter estimation in piecewise deterministic processes using particle Gibbs samplers," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 577-609, June.
    27. Stephanie Ettmeier & Alexander Kriwoluzky, 2020. "Active, or Passive? Revisiting the Role of Fiscal Policy in the Great Inflation," Discussion Papers of DIW Berlin 1872, DIW Berlin, German Institute for Economic Research.
    28. Andras Fulop & Junye Li & Jun Yu, 2012. "Investigating Impacts of Self-Exciting Jumps in Returns and Volatility: A Bayesian Learning Approach," Global COE Hi-Stat Discussion Paper Series gd12-264, Institute of Economic Research, Hitotsubashi University.
    29. Nicolas Chopin, 2002. "Central Limit Theorem for Sequential Monte Carlo Methods and its Applications to Bayesian Inference," Working Papers 2002-44, Center for Research in Economics and Statistics.
    30. Edward P. Herbst & Frank Schorfheide, 2016. "Tempered Particle Filtering," Finance and Economics Discussion Series 2016-072, Board of Governors of the Federal Reserve System (U.S.).
    31. Ajay Jasra & Kody Law & Carina Suciu, 2020. "Advanced Multilevel Monte Carlo Methods," International Statistical Review, International Statistical Institute, vol. 88(3), pages 548-579, December.
    32. James Martin & Ajay Jasra & Emma McCoy, 2013. "Inference for a class of partially observed point process models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(3), pages 413-437, June.
    33. Dan Li & Adam Clements & Christopher Drovandi, 2019. "Efficient Bayesian estimation for GARCH-type models via Sequential Monte Carlo," Papers 1906.03828, arXiv.org, revised Mar 2020.
    34. Marco J. Lombardi & Simon J. Godsill, 2004. "On-line Bayesian estimation of AR signals in symmetric alpha-stable noise," Econometrics Working Papers Archive wp2004_05, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    35. Nathaniel Tomasetti & Catherine Forbes & Anastasios Panagiotelis, 2020. "Updating Variational Bayes: Fast Sequential Posterior Inference," Monash Econometrics and Business Statistics Working Papers 27/20, Monash University, Department of Econometrics and Business Statistics.
    36. Li, Dan & Clements, Adam & Drovandi, Christopher, 2023. "A Bayesian approach for more reliable tail risk forecasts," Journal of Financial Stability, Elsevier, vol. 64(C).
    37. Li, Yong & Yu, Jun & Zeng, Tao, 2018. "Specification tests based on MCMC output," Journal of Econometrics, Elsevier, vol. 207(1), pages 237-260.
    38. Mark Bognanni & Edward P. Herbst, 2014. "Estimating (Markov-Switching) VAR Models without Gibbs Sampling: A Sequential Monte Carlo Approach," Working Papers (Old Series) 1427, Federal Reserve Bank of Cleveland.
    39. Golchi, Shirin & Campbell, David A., 2016. "Sequentially Constrained Monte Carlo," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 98-113.
    40. Fulop, Andras & Li, Junye, 2013. "Efficient learning via simulation: A marginalized resample-move approach," Journal of Econometrics, Elsevier, vol. 176(2), pages 146-161.
    41. T. -N. Nguyen & M. -N. Tran & R. Kohn, 2020. "Recurrent Conditional Heteroskedasticity," Papers 2010.13061, arXiv.org, revised Jan 2022.
    42. McGree, J.M., 2017. "Developments of the total entropy utility function for the dual purpose of model discrimination and parameter estimation in Bayesian design," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 207-225.
    43. Patrick Aschermayr & Konstantinos Kalogeropoulos, 2023. "Sequential Bayesian Learning for Hidden Semi-Markov Models," Papers 2301.10494, arXiv.org.
    44. Ho, Paul, 2023. "Global robust Bayesian analysis in large models," Journal of Econometrics, Elsevier, vol. 235(2), pages 608-642.
    45. Tsionas, Mike G., 2023. "Bayesian learning in performance. Is there any?," European Journal of Operational Research, Elsevier, vol. 311(1), pages 263-282.
    46. Drovandi, Christopher C. & McGree, James M. & Pettitt, Anthony N., 2013. "Sequential Monte Carlo for Bayesian sequentially designed experiments for discrete data," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 320-335.
    47. Zhang, Jinyu & Zhang, Qiaosen & Li, Yong & Wang, Qianchao, 2023. "Sequential Bayesian inference for agent-based models with application to the Chinese business cycle," Economic Modelling, Elsevier, vol. 126(C).
    48. P. Robins & V. E. Rapley & N. Green, 2009. "Realtime sequential inference of static parameters with expensive likelihood calculations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(5), pages 641-662, December.
    49. Emmanuel Mamatzakis & Mike G. Tsionas, 2021. "Testing for persistence in US mutual funds’ performance: a Bayesian dynamic panel model," Annals of Operations Research, Springer, vol. 299(1), pages 1203-1233, April.
    50. Creal, D., 2009. "A survey of sequential Monte Carlo methods for economics and finance," Serie Research Memoranda 0018, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    51. Xiaohong Chen & Timothy M. Christensen & Elie Tamer, 2017. "Monte Carlo confidence sets for identified sets," CeMMAP working papers 43/17, Institute for Fiscal Studies.
    52. Duan, Jin-Chuan & Fulop, Andras & Hsieh, Yu-Wei, 2020. "Data-cloning SMC2: A global optimizer for maximum likelihood estimation of latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
    53. N. Chopin & P. E. Jacob & O. Papaspiliopoulos, 2013. "SMC-super-2: an efficient algorithm for sequential analysis of state space models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(3), pages 397-426, June.
    54. Axel Finke & Ruth King & Alexandros Beskos & Petros Dellaportas, 2019. "Efficient Sequential Monte Carlo Algorithms for Integrated Population Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(2), pages 204-224, June.
    55. Emmanuel Mamatzakis & Mike Tsionas, 2018. "A Bayesian dynamic model to test persistence in funds' performance," Working Paper series 18-23, Rimini Centre for Economic Analysis.
    56. Li Ma, 2015. "Scalable Bayesian Model Averaging Through Local Information Propagation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 795-809, June.
    57. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
    58. Hai‐Dang Dau & Nicolas Chopin, 2022. "Waste‐free sequential Monte Carlo," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(1), pages 114-148, February.
    59. Vergé, Christelle & Morio, Jérôme & Moral, Pierre Del, 2016. "An island particle algorithm for rare event analysis," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 63-75.
    60. Mevin Hooten & Christopher Wikle & Michael Schwob, 2020. "Statistical Implementations of Agent‐Based Demographic Models," International Statistical Review, International Statistical Institute, vol. 88(2), pages 441-461, August.
    61. Garland Durham & John Geweke, 2013. "Adaptive Sequential Posterior Simulators for Massively Parallel Computing Environments," Working Paper Series 9, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    62. Moffa, Giusi & Kuipers, Jack, 2014. "Sequential Monte Carlo EM for multivariate probit models," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 252-272.
    63. Jacob, Pierre E., 2012. "Contributions computationnelles à la statistique Bayésienne," Economics Thesis from University Paris Dauphine, Paris Dauphine University, number 123456789/12804 edited by Robert, Christian P..
    64. Huang, Jing-Zhi & Ni, Jun & Xu, Li, 2022. "Leverage effect in cryptocurrency markets," Pacific-Basin Finance Journal, Elsevier, vol. 73(C).
    65. Jian He & Asma Khedher & Peter Spreij, 2021. "A Kalman particle filter for online parameter estimation with applications to affine models," Statistical Inference for Stochastic Processes, Springer, vol. 24(2), pages 353-403, July.
    66. Mathias Drton & Martyn Plummer, 2017. "A Bayesian information criterion for singular models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 323-380, March.
    67. Nicolas Chopin, 2007. "Dynamic Detection of Change Points in Long Time Series," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(2), pages 349-366, June.
    68. Jeremy Heng & Arnaud Doucet & Yvo Pokern, 2021. "Gibbs flow for approximate transport with applications to Bayesian computation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(1), pages 156-187, February.
    69. Chen Liu & Chao Wang & Minh-Ngoc Tran & Robert Kohn, 2023. "Deep Learning Enhanced Realized GARCH," Papers 2302.08002, arXiv.org, revised Oct 2023.
    70. Lau, F. Din-Houn & Gandy, Axel, 2014. "RMCMC: A system for updating Bayesian models," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 99-110.
    71. Elizabeth G. Ryan & Christopher C. Drovandi & James M. McGree & Anthony N. Pettitt, 2016. "A Review of Modern Computational Algorithms for Bayesian Optimal Design," International Statistical Review, International Statistical Institute, vol. 84(1), pages 128-154, April.
    72. Adrian E. Raftery & Le Bao, 2010. "Estimating and Projecting Trends in HIV/AIDS Generalized Epidemics Using Incremental Mixture Importance Sampling," Biometrics, The International Biometric Society, vol. 66(4), pages 1162-1173, December.
    73. McGrory, C.A. & Pettitt, A.N. & Titterington, D.M. & Alston, C.L. & Kelly, M., 2016. "Transdimensional sequential Monte Carlo using variational Bayes — SMCVB," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 246-254.
    74. Johansen, Adam M., 2009. "SMCTC: Sequential Monte Carlo in C++," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 30(i06).
    75. Drovandi, Christopher C. & Pettitt, Anthony N., 2011. "Likelihood-free Bayesian estimation of multivariate quantile distributions," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2541-2556, September.
    76. Kaeck, Andreas & Rodrigues, Paulo & Seeger, Norman J., 2018. "Model Complexity and Out-of-Sample Performance: Evidence from S&P 500 Index Returns," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 1-29.
    77. Speich, Matthias & Dormann, Carsten F. & Hartig, Florian, 2021. "Sequential Monte-Carlo algorithms for Bayesian model calibration – A review and method comparison✰," Ecological Modelling, Elsevier, vol. 455(C).
    78. Tsionas, Mike G., 2018. "A Bayesian approach to find Pareto optima in multiobjective programming problems using Sequential Monte Carlo algorithms," Omega, Elsevier, vol. 77(C), pages 73-79.
    79. Geweke, John & Durham, Garland, 2019. "Sequentially adaptive Bayesian learning algorithms for inference and optimization," Journal of Econometrics, Elsevier, vol. 210(1), pages 4-25.
    80. Nicolas Chopin & Pierre Jacob, 2010. "Free Energy Sequential Monte Carlo Application to Mixture Modelling," Working Papers 2010-34, Center for Research in Economics and Statistics.
    81. Jasra, Ajay & Doucet, Arnaud & Stephens, David A. & Holmes, Christopher C., 2008. "Interacting sequential Monte Carlo samplers for trans-dimensional simulation," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1765-1791, January.
    82. Li, Pei-Pei & Zhang, Yi & Zhao, Yan-Gang & Zhao, Zhao & Cai, Enjian, 2023. "An information reuse-based method for reliability updating," Reliability Engineering and System Safety, Elsevier, vol. 239(C).

Articles

  1. Chris J. Oates & Mark Girolami & Nicolas Chopin, 2017. "Control functionals for Monte Carlo integration," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 695-718, June.

    Cited by:

    1. Pierre E. Jacob & John O’Leary & Yves F. Atchadé, 2020. "Unbiased Markov chain Monte Carlo methods with couplings," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 543-600, July.
    2. Marina Riabiz & Wilson Ye Chen & Jon Cockayne & Pawel Swietach & Steven A. Niederer & Lester Mackey & Chris. J. Oates, 2022. "Optimal thinning of MCMC output," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1059-1081, September.
    3. Richard A. Davis & Thiago do Rêgo Sousa & Claudia Klüppelberg, 2021. "Indirect inference for time series using the empirical characteristic function and control variates," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(5-6), pages 653-684, September.
    4. Leluc, Rémi & Portier, François & Segers, Johan & Zhuman, Aigerim, 2022. "A Quadrature Rule combining Control Variates and Adaptive Importance Sampling," LIDAM Discussion Papers ISBA 2022018, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. L F South & T Karvonen & C Nemeth & M Girolami & C J Oates, 2022. "Semi-exact control functionals from Sard’s method [Zero-variance principle for Monte Carlo algorithms]," Biometrika, Biometrika Trust, vol. 109(2), pages 351-367.
    6. Jean-Jacques Forneron, 2019. "A Scrambled Method of Moments," Papers 1911.09128, arXiv.org.
    7. Leluc, Rémi & Dieuleveut, Aymeric & Portier, François & Segers, Johan & Zhuman, Aigerim, 2024. "Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates," LIDAM Discussion Papers ISBA 2024003, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    8. Linda Chamakh & Zolt'an Szab'o, 2021. "Kernel Minimum Divergence Portfolios," Papers 2110.09516, arXiv.org.
    9. Chamakh, Linda & Szabo, Zoltan, 2021. "Kernel minimum divergence portfolios," LSE Research Online Documents on Economics 115723, London School of Economics and Political Science, LSE Library.
    10. Marc Sabate Vidales & David Siska & Lukasz Szpruch, 2018. "Unbiased deep solvers for linear parametric PDEs," Papers 1810.05094, arXiv.org, revised Jan 2022.

  2. Mathieu Gerber & Nicolas Chopin, 2015. "Sequential quasi Monte Carlo," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(3), pages 509-579, June.

    Cited by:

    1. Dick, Josef & Rudolf, Daniel & Zhu, Houying, 2019. "A weighted discrepancy bound of quasi-Monte Carlo importance sampling," Statistics & Probability Letters, Elsevier, vol. 149(C), pages 100-106.
    2. Chopin, Nicolas & Gadat, Sébastien & Guedj, Benjamin & Guyader, Arnaud & Vernet, Elodie, 2015. "On some recent advances in high dimensional Bayesian Statistics," TSE Working Papers 15-557, Toulouse School of Economics (TSE).
    3. Karol Gellert & Erik Schlögl, 2021. "Parameter Learning and Change Detection Using a Particle Filter with Accelerated Adaptation," Risks, MDPI, vol. 9(12), pages 1-18, December.
    4. Gael M. Martin & David T. Frazier & Christian P. Robert, 2022. "Computing Bayes: From Then `Til Now," Monash Econometrics and Business Statistics Working Papers 14/22, Monash University, Department of Econometrics and Business Statistics.
    5. Crucinio, Francesca R. & Johansen, Adam M., 2023. "Properties of marginal sequential Monte Carlo methods," Statistics & Probability Letters, Elsevier, vol. 203(C).
    6. Nicolas Chopin & Mathieu Gerber, 2017. "Sequential quasi-Monte Carlo: Introduction for Non-Experts, Dimension Reduction, Application to Partly Observed Diffusion Processes," Working Papers 2017-35, Center for Research in Economics and Statistics.
    7. Pierre E. Jacob & John O’Leary & Yves F. Atchadé, 2020. "Unbiased Markov chain Monte Carlo methods with couplings," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 543-600, July.
    8. Zhijian He & Art B. Owen, 2016. "Extensible grids: uniform sampling on a space filling curve," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(4), pages 917-931, September.
    9. Fabian Goessling, 2018. "Randomized Quasi Sequential Markov Chain Monte Carlo²," CQE Working Papers 7018, Center for Quantitative Economics (CQE), University of Muenster.
    10. Frédéric Karamé, 2018. "A new particle filtering approach to estimate stochastic volatility models with Markov-switching," Post-Print hal-02296093, HAL.
    11. Gael M. Martin & David T. Frazier & Christian P. Robert, 2020. "Computing Bayes: Bayesian Computation from 1763 to the 21st Century," Monash Econometrics and Business Statistics Working Papers 14/20, Monash University, Department of Econometrics and Business Statistics.
    12. L’Ecuyer, Pierre & Munger, David & Lécot, Christian & Tuffin, Bruno, 2018. "Sorting methods and convergence rates for Array-RQMC: Some empirical comparisons," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 143(C), pages 191-201.
    13. Bhattacharya, Arnab & Wilson, Simon P., 2018. "Sequential Bayesian inference for static parameters in dynamic state space models," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 187-203.
    14. Golightly, Andrew & Bradley, Emma & Lowe, Tom & Gillespie, Colin S., 2019. "Correlated pseudo-marginal schemes for time-discretised stochastic kinetic models," Computational Statistics & Data Analysis, Elsevier, vol. 136(C), pages 92-107.
    15. Dellaportas, Petros & Titsias, Michalis K. & Petrova, Katerina & Plataniotis, Anastasios, 2023. "Scalable inference for a full multivariate stochastic volatility model," Journal of Econometrics, Elsevier, vol. 232(2), pages 501-520.
    16. Z. I. Botev, 2017. "The normal law under linear restrictions: simulation and estimation via minimax tilting," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 125-148, January.
    17. Mathieu GERBER & Nicolas CHOPIN & Nick WHITELEY, 2017. "Negative association, ordering and convergence of resampling methods," Working Papers 2017-36, Center for Research in Economics and Statistics.
    18. Duan, Jin-Chuan & Fulop, Andras & Hsieh, Yu-Wei, 2020. "Data-cloning SMC2: A global optimizer for maximum likelihood estimation of latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
    19. Jean-Jacques Forneron, 2019. "A Scrambled Method of Moments," Papers 1911.09128, arXiv.org.
    20. Fabian Goessling, 2018. "Human Capital, Growth, and Asset Prices," CQE Working Papers 6918, Center for Quantitative Economics (CQE), University of Muenster.
    21. Burkhart, Michael C., 2019. "A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding," Thesis Commons 4j3fu, Center for Open Science.
    22. Gael M. Martin & David T. Frazier & Christian P. Robert, 2021. "Approximating Bayes in the 21st Century," Monash Econometrics and Business Statistics Working Papers 24/21, Monash University, Department of Econometrics and Business Statistics.
    23. Fulop, Andras & Li, Junye, 2019. "Bayesian estimation of dynamic asset pricing models with informative observations," Journal of Econometrics, Elsevier, vol. 209(1), pages 114-138.

  3. Simon Barthelmé & Nicolas Chopin, 2014. "Expectation Propagation for Likelihood-Free Inference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 315-333, March.

    Cited by:

    1. Rodrigues, G.S. & Prangle, D. & Sisson, S.A., 2018. "Recalibration: A post-processing method for approximate Bayesian computation," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 53-66.
    2. Luis Alvarez & Cristine Pinto & Vladimir Ponczek, 2022. "Homophily in preferences or meetings? Identifying and estimating an iterative network formation model," Papers 2201.06694, arXiv.org, revised Mar 2024.
    3. Jonathan U Harrison & Ruth E Baker, 2020. "An automatic adaptive method to combine summary statistics in approximate Bayesian computation," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-21, August.
    4. Li, J. & Nott, D.J. & Fan, Y. & Sisson, S.A., 2017. "Extending approximate Bayesian computation methods to high dimensions via a Gaussian copula model," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 77-89.
    5. Gael M. Martin & David T. Frazier & Christian P. Robert, 2020. "Computing Bayes: Bayesian Computation from 1763 to the 21st Century," Monash Econometrics and Business Statistics Working Papers 14/20, Monash University, Department of Econometrics and Business Statistics.
    6. Raanju R. Sundararajan & Wagner Barreto‐Souza, 2023. "Student‐t stochastic volatility model with composite likelihood EM‐algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(1), pages 125-147, January.
    7. Alexander Buchholz & Nicolas CHOPIN, 2017. "Improving approximate Bayesian computation via quasi Monte Carlo," Working Papers 2017-37, Center for Research in Economics and Statistics.
    8. Dehideniya, Mahasen B. & Drovandi, Christopher C. & McGree, James M., 2018. "Optimal Bayesian design for discriminating between models with intractable likelihoods in epidemiology," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 277-297.
    9. Gael M. Martin & David T. Frazier & Christian P. Robert, 2021. "Approximating Bayes in the 21st Century," Monash Econometrics and Business Statistics Working Papers 24/21, Monash University, Department of Econometrics and Business Statistics.

  4. N. Chopin & P. E. Jacob & O. Papaspiliopoulos, 2013. "SMC-super-2: an efficient algorithm for sequential analysis of state space models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(3), pages 397-426, June.

    Cited by:

    1. Lux, Thomas, 2022. "Inference for Nonlinear State Space Models: A Comparison of Different Methods applied to Markov-Switching Multifractal Models," Econometrics and Statistics, Elsevier, vol. 21(C), pages 69-95.
    2. Arnaud Dufays, 2014. "On the conjugacy of off-line and on-line Sequential Monte Carlo Samplers," Working Paper Research 263, National Bank of Belgium.
    3. Xiao Zhang & Feng Ding & Ling Xu & Ahmed Alsaedi & Tasawar Hayat, 2019. "A Hierarchical Approach for Joint Parameter and State Estimation of a Bilinear System with Autoregressive Noise," Mathematics, MDPI, vol. 7(4), pages 1-17, April.
    4. Hubin, Aliaksandr & Storvik, Geir, 2018. "Mode jumping MCMC for Bayesian variable selection in GLMM," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 281-297.
    5. Schorfheide, Frank & Aruoba, Boragan & Mlikota, Marko & Villalvazo, Sergio, 2021. "SVARs With Occasionally-Binding Constraints," CEPR Discussion Papers 15923, C.E.P.R. Discussion Papers.
    6. Szczepocki Piotr, 2020. "Application of iterated filtering to stochastic volatility models based on non-Gaussian Ornstein-Uhlenbeck process," Statistics in Transition New Series, Polish Statistical Association, vol. 21(2), pages 173-187, June.
    7. Angelos Alexopoulos & Petros Dellaportas & Omiros Papaspiliopoulos, 2019. "Bayesian prediction of jumps in large panels of time series data," Papers 1904.05312, arXiv.org, revised Apr 2021.
    8. Hirokuni Iiboshi & Mototsugu Shintani & Kozo Ueda, 2018. "Estimating a Nonlinear New Keynesian Model with a Zero Lower Bound for Japan," Working Papers e120, Tokyo Center for Economic Research.
    9. Bognanni, Mark & Zito, John, 2020. "Sequential Bayesian inference for vector autoregressions with stochastic volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    10. Rubio-Ramírez, Juan Francisco & Schorfheide, Frank & Fernández-Villaverde, Jesús, 2015. "Solution and Estimation Methods for DSGE Models," CEPR Discussion Papers 11032, C.E.P.R. Discussion Papers.
    11. Ramis Khabibullin & Sergei Seleznev, 2022. "Fast Estimation of Bayesian State Space Models Using Amortized Simulation-Based Inference," Papers 2210.07154, arXiv.org.
    12. Arnaud Dufays, 2016. "Evolutionary Sequential Monte Carlo Samplers for Change-Point Models," Econometrics, MDPI, vol. 4(1), pages 1-33, March.
    13. Gareth W. Peters & Rodrigo S. Targino & Mario V. Wüthrich, 2017. "Bayesian Modelling, Monte Carlo Sampling and Capital Allocation of Insurance Risks," Risks, MDPI, vol. 5(4), pages 1-51, September.
    14. Naoki Awaya & Yasuhiro Omori, 2017. "Particle rolling MCMC with Double Block Sampling: Conditional SMC Update Approach," CIRJE F-Series CIRJE-F-1066, CIRJE, Faculty of Economics, University of Tokyo.
    15. Axel Finke & Adam Johansen & Dario Spanò, 2014. "Static-parameter estimation in piecewise deterministic processes using particle Gibbs samplers," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 577-609, June.
    16. Ajay Jasra & Kody Law & Carina Suciu, 2020. "Advanced Multilevel Monte Carlo Methods," International Statistical Review, International Statistical Institute, vol. 88(3), pages 548-579, December.
    17. Espen Bernton & Pierre E. Jacob & Mathieu Gerber & Christian P. Robert, 2019. "Approximate Bayesian computation with the Wasserstein distance," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 235-269, April.
    18. Dan Li & Adam Clements & Christopher Drovandi, 2019. "Efficient Bayesian estimation for GARCH-type models via Sequential Monte Carlo," Papers 1906.03828, arXiv.org, revised Mar 2020.
    19. Hainaut, Donatien & Moraux, Franck, 2019. "A switching self-exciting jump diffusion process for stock prices," LIDAM Reprints ISBA 2019017, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    20. Frédéric Karamé, 2018. "A new particle filtering approach to estimate stochastic volatility models with Markov-switching," Post-Print hal-02296093, HAL.
    21. Olivier Gu'eant & Jiang Pu, 2018. "Mid-price estimation for European corporate bonds: a particle filtering approach," Papers 1810.05884, arXiv.org, revised Mar 2019.
    22. Mark Bognanni & John Zito, 2019. "Sequential Bayesian Inference for Vector Autoregressions with Stochastic Volatility," Working Papers 19-29, Federal Reserve Bank of Cleveland.
    23. Man Chung Fung & Gareth W. Peters & Pavel V. Shevchenko, 2016. "A unified approach to mortality modelling using state-space framework: characterisation, identification, estimation and forecasting," Papers 1605.09484, arXiv.org.
    24. Patrick Aschermayr & Konstantinos Kalogeropoulos, 2023. "Sequential Bayesian Learning for Hidden Semi-Markov Models," Papers 2301.10494, arXiv.org.
    25. Bhattacharya, Arnab & Wilson, Simon P., 2018. "Sequential Bayesian inference for static parameters in dynamic state space models," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 187-203.
    26. Naoki Awaya & Yasuhiro Omori, 2019. "Particle rolling MCMC," CIRJE F-Series CIRJE-F-1110, CIRJE, Faculty of Economics, University of Tokyo.
    27. Andras Fulop & Jun Yu, 2014. "Bayesian Analysis of Bubbles in Asset Prices," Working Papers 04-2014, Singapore Management University, School of Economics.
    28. Nicolas Chopin & Sumeetpal S. Singh, 2013. "On the Particle Gibbs Sampler," Working Papers 2013-41, Center for Research in Economics and Statistics.
    29. Christophe Chesneau & Salima El Kolei & Fabien Navarro, 2022. "Parametric estimation of hidden Markov models by least squares type estimation and deconvolution," Statistical Papers, Springer, vol. 63(5), pages 1615-1648, October.
    30. Brignone, Riccardo & Gonzato, Luca & Lütkebohmert, Eva, 2023. "Efficient Quasi-Bayesian Estimation of Affine Option Pricing Models Using Risk-Neutral Cumulants," Journal of Banking & Finance, Elsevier, vol. 148(C).
    31. Axel Finke & Ruth King & Alexandros Beskos & Petros Dellaportas, 2019. "Efficient Sequential Monte Carlo Algorithms for Integrated Population Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(2), pages 204-224, June.
    32. Schorfheide, Frank & Mlikota, Marko, 2022. "Sequential Monte Carlo With Model Tempering," CEPR Discussion Papers 17035, C.E.P.R. Discussion Papers.
    33. Virbickaitė, Audronė & Frey, Christoph & Macedo, Demian N., 2020. "Bayesian sequential stock return prediction through copulas," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    34. Hai‐Dang Dau & Nicolas Chopin, 2022. "Waste‐free sequential Monte Carlo," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(1), pages 114-148, February.
    35. Mevin Hooten & Christopher Wikle & Michael Schwob, 2020. "Statistical Implementations of Agent‐Based Demographic Models," International Statistical Review, International Statistical Institute, vol. 88(2), pages 441-461, August.
    36. Huang, Jing-Zhi & Ni, Jun & Xu, Li, 2022. "Leverage effect in cryptocurrency markets," Pacific-Basin Finance Journal, Elsevier, vol. 73(C).
    37. Christophe Chesneau & Salima El Kolei & Fabien Navarro, 2017. "Parametric estimation of hidden Markov models by least squares type estimation and deconvolution," Working Papers 2017-66, Center for Research in Economics and Statistics.
    38. Jian He & Asma Khedher & Peter Spreij, 2021. "A Kalman particle filter for online parameter estimation with applications to affine models," Statistical Inference for Stochastic Processes, Springer, vol. 24(2), pages 353-403, July.
    39. Lau, F. Din-Houn & Gandy, Axel, 2014. "RMCMC: A system for updating Bayesian models," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 99-110.
    40. Naoki Awaya & Yasuhiro Omori, 2021. "Particle Rolling MCMC with Double-Block Sampling ," CIRJE F-Series CIRJE-F-1175, CIRJE, Faculty of Economics, University of Tokyo.
    41. Andras Fulop & Jeremy Heng & Junye Li, 2022. "Efficient Likelihood-based Estimation via Annealing for Dynamic Structural Macrofinance Models," Papers 2201.01094, arXiv.org.
    42. Kaeck, Andreas & Rodrigues, Paulo & Seeger, Norman J., 2018. "Model Complexity and Out-of-Sample Performance: Evidence from S&P 500 Index Returns," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 1-29.
    43. Virbickaitė, Audronė & Ausín, M. Concepción & Galeano, Pedro, 2020. "Copula stochastic volatility in oil returns: Approximate Bayesian computation with volatility prediction," Energy Economics, Elsevier, vol. 92(C).
    44. Murray, Lawrence M., 2015. "Bayesian State-Space Modelling on High-Performance Hardware Using LibBi," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i10).

  5. Chopin, N. & Del Moral, P. & Rubenthaler, S., 2011. "Stability of Feynman-Kac formulae with path-dependent potentials," Stochastic Processes and their Applications, Elsevier, vol. 121(1), pages 38-60, January.
    See citations under working paper version above.
  6. Nicolas Chopin & Christian P. Robert, 2010. "Properties of nested sampling," Biometrika, Biometrika Trust, vol. 97(3), pages 741-755.

    Cited by:

    1. Gelman Andrew & Robert Christian P. & Rousseau Judith, 2013. "Inherent difficulties of non-Bayesian likelihood-based inference, as revealed by an examination of a recent book by Aitkin," Statistics & Risk Modeling, De Gruyter, vol. 30(2), pages 105-120, June.
    2. Tahir Ekin & Nicholas G. Polson & Refik Soyer, 2017. "Augmented nested sampling for stochastic programs with recourse and endogenous uncertainty," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(8), pages 613-627, December.
    3. Gael M. Martin & David T. Frazier & Christian P. Robert, 2020. "Computing Bayes: Bayesian Computation from 1763 to the 21st Century," Monash Econometrics and Business Statistics Working Papers 14/20, Monash University, Department of Econometrics and Business Statistics.
    4. Christian P. Robert & Gareth Roberts, 2021. "Rao–Blackwellisation in the Markov Chain Monte Carlo Era," International Statistical Review, International Statistical Institute, vol. 89(2), pages 237-249, August.
    5. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
    6. Christian P. Robert, 2013. "Bayesian Computational Tools," Working Papers 2013-45, Center for Research in Economics and Statistics.
    7. Nicolas Chopin & Alessandra Iacobucci & Jean-Michel Marin & Kerrie L. Mengersen & Christian P. Robert & Robin Ryder & Christian Schafer, 2010. "On Particle Learning," Working Papers 2010-22, Center for Research in Economics and Statistics.

  7. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.

    Cited by:

    1. S. T. Buckland & Y. Yuan & E. Marcon, 2017. "Measuring temporal trends in biodiversity," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(4), pages 461-474, October.
    2. Athanasios Christou Micheas, 2014. "Hierarchical Bayesian modeling of marked non-homogeneous Poisson processes with finite mixtures and inclusion of covariate information," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(12), pages 2596-2615, December.
    3. Salima El Kolei, 2013. "Parametric estimation of hidden stochastic model by contrast minimization and deconvolution," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(8), pages 1031-1081, November.
    4. Nikoline N. Knudsen & Jörg Schullehner & Birgitte Hansen & Lisbeth F. Jørgensen & Søren M. Kristiansen & Denitza D. Voutchkova & Thomas A. Gerds & Per K. Andersen & Kristine Bihrmann & Morten Grønbæk , 2017. "Lithium in Drinking Water and Incidence of Suicide: A Nationwide Individual-Level Cohort Study with 22 Years of Follow-Up," IJERPH, MDPI, vol. 14(6), pages 1-13, June.
    5. Paige, John & Fuglstad, Geir-Arne & Riebler, Andrea & Wakefield, Jon, 2022. "Bayesian multiresolution modeling of georeferenced data: An extension of ‘LatticeKrig’," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    6. Fasil Wagnew & Kefyalew Addis Alene & Matthew Kelly & Darren Gray, 2023. "Geospatial Overlap of Undernutrition and Tuberculosis in Ethiopia," IJERPH, MDPI, vol. 20(21), pages 1-15, October.
    7. Jean-François Richard, 2015. "Likelihood Evaluation of High-Dimensional Spatial Latent Gaussian Models with Non-Gaussian Response Variables," Working Paper 5778, Department of Economics, University of Pittsburgh.
    8. Verzelen, Nicolas, 2010. "Data-driven neighborhood selection of a Gaussian field," Computational Statistics & Data Analysis, Elsevier, vol. 54(5), pages 1355-1371, May.
    9. Márcio Poletti Laurini, 2017. "A spatial error model with continuous random effects and an application to growth convergence," Journal of Geographical Systems, Springer, vol. 19(4), pages 371-398, October.
    10. John Haslett & Andrew C. Parnell & John Hinde & Rafael de Andrade Moral, 2022. "Modelling Excess Zeros in Count Data: A New Perspective on Modelling Approaches," International Statistical Review, International Statistical Institute, vol. 90(2), pages 216-236, August.
    11. Leonardo Padilla & Bernado Lagos‐Álvarez & Jorge Mateu & Emilio Porcu, 2020. "Space‐time autoregressive estimation and prediction with missing data based on Kalman filtering," Environmetrics, John Wiley & Sons, Ltd., vol. 31(7), November.
    12. Christian Schellhase & Göran Kauermann, 2012. "Density estimation and comparison with a penalized mixture approach," Computational Statistics, Springer, vol. 27(4), pages 757-777, December.
    13. Daniela Castro‐Camilo & Raphaël Huser & Håvard Rue, 2022. "Practical strategies for generalized extreme value‐based regression models for extremes," Environmetrics, John Wiley & Sons, Ltd., vol. 33(6), September.
    14. Joshua C.C. Chan & Angelia L. Grant, 2014. "Issues in Comparing Stochastic Volatility Models Using the Deviance Information Criterion," CAMA Working Papers 2014-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    15. Cross, Jamie L. & Hou, Chenghan & Koop, Gary & Poon, Aubrey, 2023. "Large stochastic volatility in mean VARs," Journal of Econometrics, Elsevier, vol. 236(1).
    16. Virgilio Gómez‐Rubio, 2021. "Book review of “Disease mapping: From foundations to multidimensional modeling”," Biometrics, The International Biometric Society, vol. 77(1), pages 372-373, March.
    17. Scott, Ryan P. & Scott, Tyler A., 2019. "Investing in collaboration for safety: Assessing grants to states for oil and gas distribution pipeline safety program enhancement," Energy Policy, Elsevier, vol. 124(C), pages 332-345.
    18. Lindgren, Finn & Rue, Håvard, 2015. "Bayesian Spatial Modelling with R-INLA," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i19).
    19. John M. Humphreys & Robert B. Srygley & David H. Branson, 2022. "Geographic Variation in Migratory Grasshopper Recruitment under Projected Climate Change," Geographies, MDPI, vol. 2(1), pages 1-19, January.
    20. Janet Niekerk & Haakon Bakka & Håvard Rue, 2023. "Stable Non-Linear Generalized Bayesian Joint Models for Survival-Longitudinal Data," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 102-128, February.
    21. John M. Humphreys, 2022. "Amplification in Time and Dilution in Space: Partitioning Spatiotemporal Processes to Assess the Role of Avian-Host Phylodiversity in Shaping Eastern Equine Encephalitis Virus Distribution," Geographies, MDPI, vol. 2(3), pages 1-16, July.
    22. Paciorek, Christopher J. & Lipshitz, Benjamin & Zhuo, Wei & Prabhat, . & Kaufman, Cari G. G. & Thomas, Rollin C., 2015. "Parallelizing Gaussian Process Calculations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i10).
    23. Beth E Ross & Mevin B Hooten & David N Koons, 2012. "An Accessible Method for Implementing Hierarchical Models with Spatio-Temporal Abundance Data," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-8, November.
    24. Medina-Olivares, Victor & Calabrese, Raffaella & Crook, Jonathan & Lindgren, Finn, 2023. "Joint models for longitudinal and discrete survival data in credit scoring," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1457-1473.
    25. Faustin Habyarimana & Shaun Ramroop, 2018. "Spatial Analysis of Socio-Economic and Demographic Factors Associated with Contraceptive Use among Women of Childbearing Age in Rwanda," IJERPH, MDPI, vol. 15(11), pages 1-15, October.
    26. Hubin, Aliaksandr & Storvik, Geir, 2018. "Mode jumping MCMC for Bayesian variable selection in GLMM," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 281-297.
    27. Laia Maynou & Javier Ordóñez & José Ignacio Silva, 2020. "NEET rates convergence in Europe: A regional analysis," Working Papers 2020/08, Economics Department, Universitat Jaume I, Castellón (Spain).
    28. Herbert Susmann & Monica Alexander & Leontine Alkema, 2022. "Temporal Models for Demographic and Global Health Outcomes in Multiple Populations: Introducing a New Framework to Review and Standardise Documentation of Model Assumptions and Facilitate Model Compar," International Statistical Review, International Statistical Institute, vol. 90(3), pages 437-467, December.
    29. Matias Quiroz & Mattias Villani & Robert Kohn & Minh-Ngoc Tran & Khue-Dung Dang, 2018. "Subsampling MCMC - an Introduction for the Survey Statistician," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 33-69, December.
    30. Somnath Chaudhuri & Gerard Giménez-Adsuar & Marc Saez & Maria A. Barceló, 2022. "PandemonCAT: Monitoring the COVID-19 Pandemic in Catalonia, Spain," IJERPH, MDPI, vol. 19(8), pages 1-22, April.
    31. Guowen Huang & Patrick E. Brown & Sze Hang Fu & Hwashin Hyun Shin, 2022. "Daily mortality/morbidity and air quality: Using multivariate time series with seasonally varying covariances," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(1), pages 148-174, January.
    32. Nicolas Depraetere & Martina Vandebroek, 2017. "A comparison of variational approximations for fast inference in mixed logit models," Computational Statistics, Springer, vol. 32(1), pages 93-125, March.
    33. Alberto Rodríguez-Casal & Paula Saavedra-Nieves, 2022. "Spatial distribution of invasive species: an extent of occurrence approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 416-441, June.
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    2. Huber, Florian & Zörner, Thomas O., 2019. "Threshold cointegration in international exchange rates:A Bayesian approach," International Journal of Forecasting, Elsevier, vol. 35(2), pages 458-473.
    3. Creal, D., 2009. "A survey of sequential Monte Carlo methods for economics and finance," Serie Research Memoranda 0018, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    4. N. Chopin & P. E. Jacob & O. Papaspiliopoulos, 2013. "SMC-super-2: an efficient algorithm for sequential analysis of state space models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(3), pages 397-426, June.
    5. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
    6. Christopher Nam & John Aston & Adam Johansen, 2014. "Parallel sequential Monte Carlo samplers and estimation of the number of states in a Hidden Markov Model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 553-575, June.
    7. Xiong, Yingge & Tobias, Justin L. & Mannering, Fred L., 2014. "The analysis of vehicle crash injury-severity data: A Markov switching approach with road-segment heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 109-128.
    8. Chopin, N., 2008. "On the equivalence between standard and sequentially ordered hidden Markov models," Statistics & Probability Letters, Elsevier, vol. 78(14), pages 2171-2174, October.

  9. Nicolas Chopin, 2007. "Dynamic Detection of Change Points in Long Time Series," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(2), pages 349-366, June.

    Cited by:

    1. Hongyue Zhu & Hong Jiao & Wei Gao & Xiangbin Meng, 2023. "Bayesian Change-Point Analysis Approach to Detecting Aberrant Test-Taking Behavior Using Response Times," Journal of Educational and Behavioral Statistics, , vol. 48(4), pages 490-520, August.
    2. Chopin, N. & Del Moral, P. & Rubenthaler, S., 2011. "Stability of Feynman-Kac formulae with path-dependent potentials," Stochastic Processes and their Applications, Elsevier, vol. 121(1), pages 38-60, January.
    3. Ruggieri, Eric & Antonellis, Marcus, 2016. "An exact approach to Bayesian sequential change point detection," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 71-86.
    4. Eric Ruggieri, 2018. "A pruned recursive solution to the multiple change point problem," Computational Statistics, Springer, vol. 33(2), pages 1017-1045, June.
    5. He, Zhongfang & Maheu, John M., 2010. "Real time detection of structural breaks in GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2628-2640, November.
    6. Lu Shaochuan, 2023. "Scalable Bayesian Multiple Changepoint Detection via Auxiliary Uniformisation," International Statistical Review, International Statistical Institute, vol. 91(1), pages 88-113, April.
    7. Rui Qiang & Eric Ruggieri, 2023. "Autocorrelation and Parameter Estimation in a Bayesian Change Point Model," Mathematics, MDPI, vol. 11(5), pages 1-22, February.
    8. Jian He & Asma Khedher & Peter Spreij, 2021. "A Kalman particle filter for online parameter estimation with applications to affine models," Statistical Inference for Stochastic Processes, Springer, vol. 24(2), pages 353-403, July.
    9. Yixiao Li & Gloria Lin & Thomas Lau & Ruochen Zeng, 2019. "A Review of Changepoint Detection Models," Papers 1908.07136, arXiv.org.

  10. Chopin, Nicolas & Pelgrin, Florian, 2004. "Bayesian inference and state number determination for hidden Markov models: an application to the information content of the yield curve about inflation," Journal of Econometrics, Elsevier, vol. 123(2), pages 327-344, December.

    Cited by:

    1. Gianni Amisano & Roberto Casarin, 2008. "Particle Filters for Markov-Switching Stochastic-Correlation Models," Working Papers 0814, University of Brescia, Department of Economics.
    2. Florian Pelgrin & Stéphane Adjemian, 2008. "Un regard bayésien sur les modèles dynamiques de la macroéconomie," Économie et Prévision, Programme National Persée, vol. 183(2), pages 127-152.
    3. Gary Koop & Simon M. Potter, 2004. "Forecasting and estimating multiple change-point models with an unknown number of change points," Staff Reports 196, Federal Reserve Bank of New York.
    4. Bellone, B. & Gautier, E. & Le Coent, S., 2005. "Les marchés financiers anticipent-ils les retournements conjoncturels?," Working papers 128, Banque de France.
    5. Wai‐Sum Chan & Li‐Xin Zhang & Siu Hung Cheung, 2009. "Temporal aggregation of Markov‐switching financial return models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(3), pages 359-383, May.
    6. Didier Nibbering & Richard Paap & Michel van der Wel, 2016. "A Bayesian Infinite Hidden Markov Vector Autoregressive Model," Tinbergen Institute Discussion Papers 16-107/III, Tinbergen Institute, revised 13 Oct 2017.
    7. Matthieu Lemoine & Florian Pelgrin, 2003. "Introduction aux modèles espace-état et au filtre de Kalman," Revue de l'OFCE, Presses de Sciences-Po, vol. 86(3), pages 203-229.
    8. Hugh Christensen & Simon Godsill & Richard E Turner, 2020. "Hidden Markov Models Applied To Intraday Momentum Trading With Side Information," Papers 2006.08307, arXiv.org.
    9. Fabrice Murtin, 2007. "The Structural Change and the Endogeneity Bias of the College Premium in the United States 1968-2001"," Working Papers 2007-14, Center for Research in Economics and Statistics.
    10. Creal, D., 2009. "A survey of sequential Monte Carlo methods for economics and finance," Serie Research Memoranda 0018, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    11. He, Zhongfang & Maheu, John M., 2010. "Real time detection of structural breaks in GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2628-2640, November.
    12. Christopher A. Sims & Daniel F. Waggoner & Tao Zha, 2006. "Methods for inference in large multiple-equation Markov-switching models," FRB Atlanta Working Paper 2006-22, Federal Reserve Bank of Atlanta.
    13. Christopher Nam & John Aston & Adam Johansen, 2014. "Parallel sequential Monte Carlo samplers and estimation of the number of states in a Hidden Markov Model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 553-575, June.
    14. Tsasa Vangu, Jean-Paul Kimbambu, 2014. "Diagnostic de la politique monétaire en Rép. Dém. Congo – Approche par l’Equilibre Général Dynamique Stochastique," Dynare Working Papers 38, CEPREMAP.
    15. Nicolas Chopin, 2007. "Dynamic Detection of Change Points in Long Time Series," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(2), pages 349-366, June.

  11. Nicolas Chopin, 2002. "A sequential particle filter method for static models," Biometrika, Biometrika Trust, vol. 89(3), pages 539-552, August.
    See citations under working paper version above.

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