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Omiros Papaspiliopoulos

Personal Details

First Name:Omiros
Middle Name:
Last Name:Papaspiliopoulos
Suffix:
RePEc Short-ID:ppa697
[This author has chosen not to make the email address public]
http://www.econ.upf.edu/~omiros

Affiliation

Departament d'Economia i Empresa
Universitat Pompeu Fabra
Barcelona Graduate School of Economics (Barcelona GSE)

Barcelona, Spain
http://www.econ.upf.edu/

: (34) 935 42 1766
(34)935 42 17 46
Ramon Trias Fargas 25-27, 08005 Barcelona
RePEc:edi:deupfes (more details at EDIRC)

Research output

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Jump to: Articles

Articles

  1. C. Yau & O. Papaspiliopoulos & G. O. Roberts & C. Holmes, 2011. "Bayesian non‐parametric hidden Markov models with applications in genomics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 37-57, January.
  2. Paul Fearnhead & Omiros Papaspiliopoulos & Gareth O. Roberts & Andrew Stuart, 2010. "Random‐weight particle filtering of continuous time processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 497-512, September.
  3. Paul Fearnhead & Omiros Papaspiliopoulos & Gareth O. Roberts, 2008. "Particle filters for partially observed diffusions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 755-777, September.
  4. Omiros Papaspiliopoulos & Gareth O. Roberts, 2008. "Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models," Biometrika, Biometrika Trust, vol. 95(1), pages 169-186.
  5. Alexandros Beskos & Omiros Papaspiliopoulos & Gareth O. Roberts & Paul Fearnhead, 2006. "Exact and computationally efficient likelihood‐based estimation for discretely observed diffusion processes (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 333-382, June.
  6. Gareth O. Roberts & Omiros Papaspiliopoulos & Petros Dellaportas, 2004. "Bayesian inference for non‐Gaussian Ornstein–Uhlenbeck stochastic volatility processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(2), pages 369-393, May.

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.

Articles

  1. C. Yau & O. Papaspiliopoulos & G. O. Roberts & C. Holmes, 2011. "Bayesian non‐parametric hidden Markov models with applications in genomics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 37-57, January.

    Cited by:

    1. Bartolucci, Francesco & Farcomeni, Alessio & Pennoni, Fulvia, 2012. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," MPRA Paper 39023, University Library of Munich, Germany.
    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. Stefano Favaro & Antonio Lijoi & Igor Prünster, 2012. "On the stick–breaking representation of normalized inverse Gaussian priors," DEM Working Papers Series 008, University of Pavia, Department of Economics and Management.
    4. Laura Liu, 2018. "Density Forecasts in Panel Data Models : A Semiparametric Bayesian Perspective," Finance and Economics Discussion Series 2018-036, Board of Governors of the Federal Reserve System (US).
    5. Ng, Jason & Forbes, Catherine S. & Martin, Gael M. & McCabe, Brendan P.M., 2013. "Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 411-430.
    6. Laura Liu, 2018. "Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective," Papers 1805.04178, arXiv.org.
    7. Raffaele Argiento & Matteo Ruggiero, 2018. "Computational challenges and temporal dependence in Bayesian nonparametric models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 231-238, June.
    8. Laura Liu, 2017. "Density Forecasts in Panel Models: A semiparametric Bayesian Perspective," PIER Working Paper Archive 17-006, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 28 Apr 2017.
    9. Liverani, Silvia & Hastie, David I. & Azizi, Lamiae & Papathomas, Michail & Richardson, Sylvia, 2015. "PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i07).
    10. Xia, Ye-Mao & Tang, Nian-Sheng, 2019. "Bayesian analysis for mixture of latent variable hidden Markov models with multivariate longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 190-211.

  2. Paul Fearnhead & Omiros Papaspiliopoulos & Gareth O. Roberts, 2008. "Particle filters for partially observed diffusions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 755-777, September.

    Cited by:

    1. Mark Briers & Arnaud Doucet & Simon Maskell, 2010. "Smoothing algorithms for state–space models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 61-89, February.
    2. Alexandros Beskos & Omiros Papaspiliopoulos & Gareth O. Roberts & Paul Fearnhead, 2006. "Exact and computationally efficient likelihood‐based estimation for discretely observed diffusion processes (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 333-382, June.
    3. Shoji, Isao, 2013. "Filtering for partially observed diffusion and its applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 4966-4976.
    4. Kalogeropoulos, Konstantinos & Roberts, Gareth O. & Dellaportas, Petros, 2010. "Inference for stochastic volatility models using time change transformations," LSE Research Online Documents on Economics 31421, London School of Economics and Political Science, LSE Library.
    5. Johansen, Adam M. & Doucet, Arnaud, 2008. "A note on auxiliary particle filters," Statistics & Probability Letters, Elsevier, vol. 78(12), pages 1498-1504, September.
    6. Jourdain Benjamin & Sbai Mohamed, 2007. "Exact retrospective Monte Carlo computation of arithmetic average Asian options," Monte Carlo Methods and Applications, De Gruyter, vol. 13(2), pages 135-171, July.
    7. Stefano Iacus & Masayuki Uchida & Nakahiro Yoshida, 2006. "Parametric estimation for partially hidden diffusion processes sampled at discrete times," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1042, Universitá degli Studi di Milano.
    8. Paul Fearnhead & Omiros Papaspiliopoulos & Gareth O. Roberts & Andrew Stuart, 2010. "Random‐weight particle filtering of continuous time processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 497-512, September.
    9. 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.
    10. Peter J. Diggle & Raquel Menezes & Ting-li Su, 2010. "Geostatistical inference under preferential sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 191-232.
    11. Hermann Singer, 2011. "Continuous-discrete state-space modeling of panel data with nonlinear filter algorithms," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 375-413, December.
    12. 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).

  3. Omiros Papaspiliopoulos & Gareth O. Roberts, 2008. "Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models," Biometrika, Biometrika Trust, vol. 95(1), pages 169-186.

    Cited by:

    1. Audrone Virbickaite & M. Concepción Ausín & Pedro Galeano, 2015. "Bayesian Inference Methods For Univariate And Multivariate Garch Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 76-96, February.
    2. Virbickaitė, Audronė & Ausín, M. Concepción & Galeano, Pedro, 2016. "A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 814-829.
    3. Mahdi Hosseinpouri & Majid Jafari Khaledi, 2019. "An area-specific stick breaking process for spatial data," Statistical Papers, Springer, vol. 60(1), pages 199-221, February.
    4. Jim E. Griffin & Fabrizio Leisen, 2017. "Compound random measures and their use in Bayesian non-parametrics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 525-545, March.
    5. Ausín, M. Concepción & Galeano, Pedro & Ghosh, Pulak, 2014. "A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation," European Journal of Operational Research, Elsevier, vol. 232(2), pages 350-358.
    6. Kalogeropoulos, Konstantinos & Roberts, Gareth O. & Dellaportas, Petros, 2010. "Inference for stochastic volatility models using time change transformations," LSE Research Online Documents on Economics 31421, London School of Economics and Political Science, LSE Library.
    7. Stefano Favaro & Antonio Lijoi & Igor Prünster, 2012. "On the stick–breaking representation of normalized inverse Gaussian priors," DEM Working Papers Series 008, University of Pavia, Department of Economics and Management.
    8. Richard F. MacLehose & David B. Dunson, 2010. "Bayesian Semiparametric Multiple Shrinkage," Biometrics, The International Biometric Society, vol. 66(2), pages 455-462, June.
    9. Roberto Casarin & Federico Bassetti & Francesco Ravazzolo, 2015. "Bayesian Nonparametric Calibration and Combination of Predictive Distributions," Working Papers 2015:04, Department of Economics, University of Venice "Ca' Foscari".
    10. Sun Jiehuan & Warren Joshua L. & Zhao Hongyu, 2017. "A Bayesian semiparametric factor analysis model for subtype identification," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(2), pages 145-158, April.
    11. Lancelot F. James & Antonio Lijoi & Igor Prünster, 2009. "Posterior Analysis for Normalized Random Measures with Independent Increments," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(1), pages 76-97.
    12. Pelenis, Justinas, 2014. "Bayesian regression with heteroscedastic error density and parametric mean function," Journal of Econometrics, Elsevier, vol. 178(P3), pages 624-638.
    13. Wiper, Michael Peter & Zhao, Yanyun & Ausín Olivera, María Concepción, 2013. "Bayesian multivariate Bernstein polynomial density estimation," DES - Working Papers. Statistics and Econometrics. WS ws131211, Universidad Carlos III de Madrid. Departamento de Estadística.
    14. Laura Liu, 2018. "Density Forecasts in Panel Data Models : A Semiparametric Bayesian Perspective," Finance and Economics Discussion Series 2018-036, Board of Governors of the Federal Reserve System (US).
    15. Crespo Cuaresma, Jesus & Grün, Bettina & Hofmarcher, Paul & Humer, Stefan & Moser, Mathias, 2016. "Unveiling covariate inclusion structures in economic growth regressions using latent class analysis," European Economic Review, Elsevier, vol. 81(C), pages 189-202.
    16. Miller, Jeffrey W., 2019. "An elementary derivation of the Chinese restaurant process from Sethuraman’s stick-breaking process," Statistics & Probability Letters, Elsevier, vol. 146(C), pages 112-117.
    17. Yang, Mingan, 2012. "Bayesian variable selection for logistic mixed model with nonparametric random effects," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2663-2674.
    18. Debdeep Pati & David Dunson, 2014. "Bayesian nonparametric regression with varying residual density," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(1), pages 1-31, February.
    19. Laura Liu, 2018. "Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective," Papers 1805.04178, arXiv.org.
    20. Stefano Tonellato, 2019. "Bayesian nonparametric clustering as a community detection problem," Working Papers 2019: 20, Department of Economics, University of Venice "Ca' Foscari".
    21. Luis E. Nieto-Barajas & Peter Müller & Yuan Ji & Yiling Lu & Gordon B. Mills, 2012. "A Time-Series DDP for Functional Proteomics Profiles," Biometrics, The International Biometric Society, vol. 68(3), pages 859-868, September.
    22. Pelenis, Justinas, 2012. "Bayesian Semiparametric Regression," Economics Series 285, Institute for Advanced Studies.
    23. Laura Liu, 2017. "Density Forecasts in Panel Models: A semiparametric Bayesian Perspective," PIER Working Paper Archive 17-006, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 28 Apr 2017.
    24. Cai, Bo & Meyer, Renate, 2011. "Bayesian semiparametric modeling of survival data based on mixtures of B-spline distributions," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1260-1272, March.
    25. Monica Billio & Roberto Casarin & Luca Rossini, 2016. "Bayesian nonparametric sparse seemingly unrelated regression model (SUR)," Working Papers 2016:20, Department of Economics, University of Venice "Ca' Foscari".
    26. Monica Billio & Roberto Casarin & Luca Rossini, 2016. "Bayesian nonparametric sparse VAR models," Papers 1608.02740, arXiv.org, revised Oct 2018.
    27. Sylvia Frühwirth-Schnatter & Gertraud Malsiner-Walli, 2019. "From here to infinity: sparse finite versus Dirichlet process mixtures in model-based clustering," 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. 13(1), pages 33-64, March.
    28. Isadora Antoniano-Villalobos & Stephen G. Walker, 2016. "A Nonparametric Model for Stationary Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(1), pages 126-142, January.
    29. Liverani, Silvia & Hastie, David I. & Azizi, Lamiae & Papathomas, Michail & Richardson, Sylvia, 2015. "PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i07).
    30. Li, Mingyang & Meng, Hongdao & Zhang, Qingpeng, 2017. "A nonparametric Bayesian modeling approach for heterogeneous lifetime data with covariates," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 95-104.
    31. Rebecca Graziani & Michele Guindani & Peter F. Thall, 2015. "Bayesian nonparametric estimation of targeted agent effects on biomarker change to predict clinical outcome," Biometrics, The International Biometric Society, vol. 71(1), pages 188-197, March.
    32. C. Yau & O. Papaspiliopoulos & G. O. Roberts & C. Holmes, 2011. "Bayesian non‐parametric hidden Markov models with applications in genomics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 37-57, January.
    33. Mingan Yang & David Dunson, 2010. "Bayesian Semiparametric Structural Equation Models with Latent Variables," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 675-693, December.

  4. Alexandros Beskos & Omiros Papaspiliopoulos & Gareth O. Roberts & Paul Fearnhead, 2006. "Exact and computationally efficient likelihood‐based estimation for discretely observed diffusion processes (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 333-382, June.

    Cited by:

    1. Isambi Mbalawata & Simo Särkkä & Heikki Haario, 2013. "Parameter estimation in stochastic differential equations with Markov chain Monte Carlo and non-linear Kalman filtering," Computational Statistics, Springer, vol. 28(3), pages 1195-1223, June.
    2. Umberto Picchini & Andrea De Gaetano & Susanne Ditlevsen, 2010. "Stochastic Differential Mixed-Effects Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(1), pages 67-90.
    3. Nina Munkholt Jakobsen & Michael Sørensen, 2015. "Efficient Estimation for Diffusions Sampled at High Frequency Over a Fixed Time Interval," CREATES Research Papers 2015-33, Department of Economics and Business Economics, Aarhus University.
    4. Comte, F. & Genon-Catalot, V. & Rozenholc, Y., 2009. "Nonparametric adaptive estimation for integrated diffusions," Stochastic Processes and their Applications, Elsevier, vol. 119(3), pages 811-834, March.
    5. Kyoung-Kuk Kim & Sojung Kim, 2016. "Simulation of Tempered Stable Lévy Bridges and Its Applications," Operations Research, INFORMS, vol. 64(2), pages 495-509, April.
    6. Qihong Duan & Junrong Liu, 2015. "A first step to implement Gillespie’s algorithm with rejection sampling," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(1), pages 85-95, March.
    7. Shoji, Isao, 2013. "Filtering for partially observed diffusion and its applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 4966-4976.
    8. Hermann Singer, 2014. "Importance sampling for Kolmogorov backward equations," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 98(4), pages 345-369, October.
    9. Kalogeropoulos, Konstantinos & Roberts, Gareth O. & Dellaportas, Petros, 2010. "Inference for stochastic volatility models using time change transformations," LSE Research Online Documents on Economics 31421, London School of Economics and Political Science, LSE Library.
    10. Paul Fearnhead & Omiros Papaspiliopoulos & Gareth O. Roberts, 2008. "Particle filters for partially observed diffusions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 755-777, September.
    11. Markussen, Bo, 2009. "Laplace approximation of transition densities posed as Brownian expectations," Stochastic Processes and their Applications, Elsevier, vol. 119(1), pages 208-231, January.
    12. Gareth W. Peters & Rodrigo S. Targino & Mario V. Wüthrich, 2017. "Bayesian Modelling, Monte Carlo Sampling and Capital Allocation of Insurance Risks," Risks, MDPI, Open Access Journal, vol. 5(4), pages 1-51, September.
    13. Kalogeropoulos, Konstantinos & Dellaportas, Petros & Roberts, Gareth O., 2007. "Likelihood-based inference for correlated diffusions," MPRA Paper 5696, University Library of Munich, Germany.
    14. Vinícius Diniz Mayrink & Flávio Bambirra Gonçalves, 2017. "A Bayesian hidden Markov mixture model to detect overexpressed chromosome regions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(2), pages 387-412, February.
    15. Aleksandar Mijatovic & Paul Schneider, 2009. "Empirical asset pricing with nonlinear risk premia," Papers 0911.0928, arXiv.org.
    16. Julie Lyng Forman & Michael Sørensen, 2008. "The Pearson Diffusions: A Class of Statistically Tractable Diffusion Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 438-465.
    17. Kalogeropoulos, Konstantinos, 2007. "Likelihood-based inference for a class of multivariate diffusions with unobserved paths," LSE Research Online Documents on Economics 31423, London School of Economics and Political Science, LSE Library.
    18. Picchini, Umberto & Anderson, Rachele, 2017. "Approximate maximum likelihood estimation using data-cloning ABC," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 166-183.
    19. Paul Fearnhead & Vasilieos Giagos & Chris Sherlock, 2014. "Inference for reaction networks using the linear noise approximation," Biometrics, The International Biometric Society, vol. 70(2), pages 457-466, June.
    20. Czellar, Veronika & Karolyi, G. Andrew & Ronchetti, Elvezio, 2005. "Indirect Robust Estimation of the Short-term Interest Rate Process," Working Paper Series 2005-4, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
    21. Sy-Miin Chow & Zhaohua Lu & Andrew Sherwood & Hongtu Zhu, 2016. "Fitting Nonlinear Ordinary Differential Equation Models with Random Effects and Unknown Initial Conditions Using the Stochastic Approximation Expectation–Maximization (SAEM) Algorithm," Psychometrika, Springer;The Psychometric Society, vol. 81(1), pages 102-134, March.
    22. Yuan Shen & Dan Cornford & Manfred Opper & Cedric Archambeau, 2012. "Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions," Computational Statistics, Springer, vol. 27(1), pages 149-176, March.
    23. Varughese, Melvin M., 2013. "Parameter estimation for multivariate diffusion systems," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 417-428.
    24. Beskos, Alexandros & Kalogeropoulos, Konstantinos & Pazos, Erik, 2013. "Advanced MCMC methods for sampling on diffusion pathspace," Stochastic Processes and their Applications, Elsevier, vol. 123(4), pages 1415-1453.
    25. Theodore Simos & Mike Tsionas, 2018. "Bayesian inference of the fractional Ornstein–Uhlenbeck process under a flow sampling scheme," Computational Statistics, Springer, vol. 33(4), pages 1687-1713, December.
    26. Masayuki Uchida, 2010. "Contrast-based information criterion for ergodic diffusion processes from discrete observations," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 161-187, February.
    27. Nafidi, A. & Gutiérrez, R. & Gutiérrez-Sánchez, R. & Ramos-Ábalos, E. & El Hachimi, S., 2016. "Modelling and predicting electricity consumption in Spain using the stochastic Gamma diffusion process with exogenous factors," Energy, Elsevier, vol. 113(C), pages 309-318.
    28. Golightly, A. & Wilkinson, D.J., 2008. "Bayesian inference for nonlinear multivariate diffusion models observed with error," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1674-1693, January.
    29. Lee, Yoon Dong & Song, Seongjoo & Lee, Eun-Kyung, 2014. "The delta expansion for the transition density of diffusion models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 694-705.
    30. Mogens Bladt & Samuel Finch & Michael Sørensen, 2016. "Simulation of multivariate diffusion bridges," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 343-369, March.
    31. Bin Zhu & Peter X.-K. Song & Jeremy M.G. Taylor, 2011. "Stochastic Functional Data Analysis: A Diffusion Model-Based Approach," Biometrics, The International Biometric Society, vol. 67(4), pages 1295-1304, December.
    32. Rosen, Ori & Thompson, Wesley K., 2009. "A Bayesian regression model for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3773-3786, September.
    33. 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.
    34. Paul Fearnhead & Omiros Papaspiliopoulos & Gareth O. Roberts & Andrew Stuart, 2010. "Random‐weight particle filtering of continuous time processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 497-512, September.
    35. van der Meulen, Frank & Schauer, Moritz & van Zanten, Harry, 2014. "Reversible jump MCMC for nonparametric drift estimation for diffusion processes," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 615-632.
    36. Yvo Pokern & Andrew M. Stuart & Petter Wiberg, 2009. "Parameter estimation for partially observed hypoelliptic diffusions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 49-73, January.
    37. Chang, Jinyuan & Chen, Songxi, 2011. "On the Approximate Maximum Likelihood Estimation for Diffusion Processes," MPRA Paper 46279, University Library of Munich, Germany.
    38. Peter J. Diggle & Raquel Menezes & Ting-li Su, 2010. "Geostatistical inference under preferential sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 191-232.
    39. Michael Sørensen, 2008. "Parametric inference for discretely sampled stochastic differential equations," CREATES Research Papers 2008-18, Department of Economics and Business Economics, Aarhus University.
    40. Isadora Antoniano-Villalobos & Stephen G. Walker, 2016. "A Nonparametric Model for Stationary Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(1), pages 126-142, January.
    41. Mogens Bladt & Samuel Finch & Michael Sørensen, 2014. "Simulation of multivariate diffusion bridges," CREATES Research Papers 2014-16, Department of Economics and Business Economics, Aarhus University.
    42. J. O. Ramsay & G. Hooker & D. Campbell & J. Cao, 2007. "Parameter estimation for differential equations: a generalized smoothing approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 741-796, November.
    43. S. C. Kou & Benjamin P. Olding & Martin Lysy & Jun S. Liu, 2012. "A Multiresolution Method for Parameter Estimation of Diffusion Processes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1558-1574, December.
    44. Giorgos Sermaidis & Omiros Papaspiliopoulos & Gareth O. Roberts & Alexandros Beskos & Paul Fearnhead, 2013. "Markov Chain Monte Carlo for Exact Inference for Diffusions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(2), pages 294-321, June.
    45. DiCesare, Joe & Mcleish, Don, 2008. "Simulation of jump diffusions and the pricing of options," Insurance: Mathematics and Economics, Elsevier, vol. 43(3), pages 316-326, December.
    46. Hermann Singer, 2011. "Continuous-discrete state-space modeling of panel data with nonlinear filter algorithms," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 375-413, December.
    47. Christian P. Robert, 2013. "Bayesian Computational Tools," Working Papers 2013-45, Center for Research in Economics and Statistics.
    48. 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. Gareth O. Roberts & Omiros Papaspiliopoulos & Petros Dellaportas, 2004. "Bayesian inference for non‐Gaussian Ornstein–Uhlenbeck stochastic volatility processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(2), pages 369-393, May.

    Cited by:

    1. Almut E. D. Veraart & Luitgard A. M. Veraart, 2009. "Stochastic volatility and stochastic leverage," CREATES Research Papers 2009-20, Department of Economics and Business Economics, Aarhus University.
    2. Marco Minozzo & Silvia Centanni, 2012. "Monte Carlo likelihood inference for marked doubly stochastic Poisson processes with intensity driven by marked point processes," Working Papers 11/2012, University of Verona, Department of Economics.
    3. Ole E. Barndorff-Nielsen & Elisa Nicolato & Neil Shephard, 2001. "Some recent developments in stochastic volatility modelling," Economics Papers 2001-W25, Economics Group, Nuffield College, University of Oxford.
    4. Griffin, Jim & Steel, Mark F.J., 2008. "Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes," MPRA Paper 11071, University Library of Munich, Germany.
    5. Lancelot F. James, 2005. "Analysis of a Class of Likelihood Based Continuous Time Stochastic Volatility Models including Ornstein-Uhlenbeck Models in Financial Economics," Papers math/0503055, arXiv.org, revised Aug 2005.
    6. Almut E. D. Veraart, 2008. "Impact of time–inhomogeneous jumps and leverage type effects on returns and realised variances," CREATES Research Papers 2008-57, Department of Economics and Business Economics, Aarhus University.
    7. Emanuele Taufer, 2008. "Characteristic function estimation of non-Gaussian Ornstein-Uhlenbeck processes," DISA Working Papers 0805, Department of Computer and Management Sciences, University of Trento, Italy, revised 07 Jul 2008.
    8. Gregor Kastner & Sylvia Fruhwirth-Schnatter, 2017. "Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models," Papers 1706.05280, arXiv.org.
    9. Roberto Leon-Gonzalez, 2015. "Efficient Bayesian Inference in Generalized Inverse Gamma Processes for Stochastic Volatility," GRIPS Discussion Papers 15-17, National Graduate Institute for Policy Studies.
    10. Chris M Strickland & Gael Martin & Catherine S Forbes, 2006. "Parameterisation and Efficient MCMC Estimation of Non-Gaussian State Space Models," Monash Econometrics and Business Statistics Working Papers 22/06, Monash University, Department of Econometrics and Business Statistics.
    11. Gonzalez, Jhonny & Moriarty, John & Palczewski, Jan, 2017. "Bayesian calibration and number of jump components in electricity spot price models," Energy Economics, Elsevier, vol. 65(C), pages 375-388.
    12. Creal, Drew D., 2008. "Analysis of filtering and smoothing algorithms for Lévy-driven stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2863-2876, February.
    13. Zhongxian Men & Tony S. Wirjanto & Adam W. Kolkiewicz, 2016. "A Multiscale Stochastic Conditional Duration Model," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 11(04), pages 1-28, December.
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