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Citations

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Cited by:

  1. Matthew Pietrosanu & Jueyu Gao & Linglong Kong & Bei Jiang & Di Niu, 2021. "Advanced algorithms for penalized quantile and composite quantile regression," Computational Statistics, Springer, vol. 36(1), pages 333-346, March.
  2. Carlo Albert & Sören Vogel & Roman Ashauer, 2016. "Computationally Efficient Implementation of a Novel Algorithm for the General Unified Threshold Model of Survival (GUTS)," PLOS Computational Biology, Public Library of Science, vol. 12(6), pages 1-19, June.
  3. Anderson, Eric C. & Ng, Thomas C., 2016. "Bayesian pedigree inference with small numbers of single nucleotide polymorphisms via a factor-graph representation," Theoretical Population Biology, Elsevier, vol. 107(C), pages 39-51.
  4. Wilson J. Wright & Peter N. Neitlich & Alyssa E. Shiel & Mevin B. Hooten, 2022. "Mechanistic spatial models for heavy metal pollution," Environmetrics, John Wiley & Sons, Ltd., vol. 33(8), December.
  5. DiTraglia, Francis J., 2016. "Using invalid instruments on purpose: Focused moment selection and averaging for GMM," Journal of Econometrics, Elsevier, vol. 195(2), pages 187-208.
  6. Anoek Castelein & Dennis Fok & Richard Paap, 2020. "A multinomial and rank-ordered logit model with inter- and intra-individual heteroscedasticity," Tinbergen Institute Discussion Papers 20-069/III, Tinbergen Institute.
  7. Steven Campbell & Ting-Kam Leonard Wong, 2022. "Efficient Convex PCA with applications to Wasserstein geodesic PCA and ranked data," Papers 2211.02990, arXiv.org, revised Aug 2023.
  8. Cheng, Ching-Wei & Hung, Ying-Chao & Balakrishnan, Narayanaswamy, 2014. "Generating beta random numbers and Dirichlet random vectors in R: The package rBeta2009," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1011-1020.
  9. Augustyniak, Maciej, 2014. "Maximum likelihood estimation of the Markov-switching GARCH model," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 61-75.
  10. Park, Jaewoo & Jin, Ick Hoon & Schweinberger, Michael, 2022. "Bayesian model selection for high-dimensional Ising models, with applications to educational data," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).
  11. Justyna Klejdysz & Robin L. Lumsdaine, 2023. "Shifts in ECB Communication: A Textual Analysis of the Press Conference," International Journal of Central Banking, International Journal of Central Banking, vol. 19(2), pages 473-542, June.
  12. Lydia Simon & Jost Adler, 2022. "Worth the effort? Comparison of different MCMC algorithms for estimating the Pareto/NBD model," Journal of Business Economics, Springer, vol. 92(4), pages 707-733, May.
  13. George Gerogiannis & Mark Tranmer & Duncan Lee & Thomas Valente, 2022. "A Bayesian spatio‐network model for multiple adolescent adverse health behaviours," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 271-287, March.
  14. Rathelot, Roland, 2014. "Ethnic differentials on the labor market in the presence of asymmetric spatial sorting: Set identification and estimation," Regional Science and Urban Economics, Elsevier, vol. 48(C), pages 154-167.
  15. Jos'e Vin'icius de Miranda Cardoso & Jiaxi Ying & Daniel Perez Palomar, 2020. "Algorithms for Learning Graphs in Financial Markets," Papers 2012.15410, arXiv.org.
  16. Lizhen Lin & Brian St. Thomas & Hongtu Zhu & David B. Dunson, 2017. "Extrinsic Local Regression on Manifold-Valued Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1261-1273, July.
  17. Lucey, Sean M. & Gaichas, Sarah K. & Aydin, Kerim Y., 2020. "Conducting reproducible ecosystem modeling using the open source mass balance model Rpath," Ecological Modelling, Elsevier, vol. 427(C).
  18. Catalina A Vallejos & John C Marioni & Sylvia Richardson, 2015. "BASiCS: Bayesian Analysis of Single-Cell Sequencing Data," PLOS Computational Biology, Public Library of Science, vol. 11(6), pages 1-18, June.
  19. Hancock, Thomas O. & Hess, Stephane & Marley, A.A.J. & Choudhury, Charisma F., 2021. "An accumulation of preference: Two alternative dynamic models for understanding transport choices," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 250-282.
  20. Shen, Yunyi & Olson, Erik R. & Van Deelen, Timothy R., 2021. "Spatially explicit modeling of community occupancy using Markov Random Field models with imperfect observation: Mesocarnivores in Apostle Islands National Lakeshore," Ecological Modelling, Elsevier, vol. 459(C).
  21. Adrien Ickowicz & Jessica Ford & Keith Hayes, 2019. "A Mixture Model Approach for Compositional Data: Inferring Land-Use Influence on Point-Referenced Water Quality Measurements," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(4), pages 719-739, December.
  22. Thomas Stringham, 2022. "Fast Bayesian Record Linkage With Record-Specific Disagreement Parameters," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1509-1522, October.
  23. Steven Andrew Culpepper, 2015. "Bayesian Estimation of the DINA Model With Gibbs Sampling," Journal of Educational and Behavioral Statistics, , vol. 40(5), pages 454-476, October.
  24. Kreuzer, Alexander & Czado, Claudia, 2021. "Bayesian inference for a single factor copula stochastic volatility model using Hamiltonian Monte Carlo," Econometrics and Statistics, Elsevier, vol. 19(C), pages 130-150.
  25. Geniaux, Ghislain & Martinetti, Davide, 2018. "A new method for dealing simultaneously with spatial autocorrelation and spatial heterogeneity in regression models," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 74-85.
  26. Bhattacharya, Arnab & Wilson, Simon P. & Soyer, Refik, 2019. "A Bayesian approach to modeling mortgage default and prepayment," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1112-1124.
  27. Wenbo Wu & Jeremy M. G. Taylor & Andrew F. Brouwer & Lingfeng Luo & Jian Kang & Hui Jiang & Kevin He, 2022. "Scalable proximal methods for cause-specific hazard modeling with time-varying coefficients," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(2), pages 194-218, April.
  28. Fernández de Marcos Giménez de los Galanes, Alberto & García Portugués, Eduardo, 2022. "Data-driven stabilizations of goodness-of-fit tests," DES - Working Papers. Statistics and Econometrics. WS 35324, Universidad Carlos III de Madrid. Departamento de Estadística.
  29. Julien Boelaert, 2013. "A Neural Network Demand System," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00917810, HAL.
  30. Kastner, Gregor & Frühwirth-Schnatter, Sylvia, 2014. "Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 408-423.
  31. Vuorre, Matti & Bolger, Niall, 2017. "Within-subject mediation analysis," OSF Preprints s48e2, Center for Open Science.
  32. Mirshani, Ardalan & Reimherr, Matthew, 2021. "Adaptive function-on-scalar regression with a smoothing elastic net," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
  33. Kris Boudt & Jon Danielsson & Siem Jan Koopman & Andre Lucas, 2012. "Regime switches in the volatility and correlation of financial institutions," Working Paper Research 227, National Bank of Belgium.
  34. Buddhavarapu, Prasad & Scott, James G. & Prozzi, Jorge A., 2016. "Modeling unobserved heterogeneity using finite mixture random parameters for spatially correlated discrete count data," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 492-510.
  35. David Kahle & Ruriko Yoshida & Luis Garcia-Puente, 2018. "Hybrid schemes for exact conditional inference in discrete exponential families," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(5), pages 983-1011, October.
  36. Markus Jochmann, 2013. "What belongs where? Variable selection for zero-inflated count models with an application to the demand for health care," Computational Statistics, Springer, vol. 28(5), pages 1947-1964, October.
  37. James Joseph Balamuta & Steven Andrew Culpepper, 2022. "Exploratory Restricted Latent Class Models with Monotonicity Requirements under PÒLYA–GAMMA Data Augmentation," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 903-945, September.
  38. Athanasios C. Micheas & Jiaxun Chen, 2018. "sppmix: Poisson point process modeling using normal mixture models," Computational Statistics, Springer, vol. 33(4), pages 1767-1798, December.
  39. Sai K. Popuri & Andrew M. Raim & Nagaraj K. Neerchal & Matthias K. Gobbert, 2017. "Parallelizing Computation of Expected Values in Recombinant Binomial Trees," Papers 1701.03512, arXiv.org, revised Oct 2018.
  40. Bandara, Kanchana & Varpe, Øystein & Ji, Rubao & Eiane, Ketil, 2018. "A high-resolution modeling study on diel and seasonal vertical migrations of high-latitude copepods," Ecological Modelling, Elsevier, vol. 368(C), pages 357-376.
  41. Carter Allen & Yuzhou Chang & Brian Neelon & Won Chang & Hang J. Kim & Zihai Li & Qin Ma & Dongjun Chung, 2023. "A Bayesian multivariate mixture model for high throughput spatial transcriptomics," Biometrics, The International Biometric Society, vol. 79(3), pages 1775-1787, September.
  42. Lee, Xing Ju & Hainy, Markus & McKeone, James P. & Drovandi, Christopher C. & Pettitt, Anthony N., 2018. "ABC model selection for spatial extremes models applied to South Australian maximum temperature data," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 128-144.
  43. Quentin F. Gronau & Eric-Jan Wagenmakers & Daniel W. Heck & Dora Matzke, 2019. "A Simple Method for Comparing Complex Models: Bayesian Model Comparison for Hierarchical Multinomial Processing Tree Models Using Warp-III Bridge Sampling," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 261-284, March.
  44. Pechon, Florian & Denuit, Michel & Trufin, Julien, 2019. "Home and Motor insurance joined at a household level using multivariate credibility," LIDAM Discussion Papers ISBA 2019013, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
  45. Sloot Henrik, 2022. "Implementing Markovian models for extendible Marshall–Olkin distributions," Dependence Modeling, De Gruyter, vol. 10(1), pages 308-343, January.
  46. Molloy, Joseph & Becker, Felix & Schmid, Basil & Axhausen, Kay W., 2021. "mixl: An open-source R package for estimating complex choice models on large datasets," Journal of choice modelling, Elsevier, vol. 39(C).
  47. Klemen Pavlič & Torben Martinussen & Per Kragh Andersen, 2019. "Goodness of fit tests for estimating equations based on pseudo-observations," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(2), pages 189-205, April.
  48. Shaobo Jin & Irini Moustaki & Fan Yang-Wallentin, 2018. "Approximated Penalized Maximum Likelihood for Exploratory Factor Analysis: An Orthogonal Case," Psychometrika, Springer;The Psychometric Society, vol. 83(3), pages 628-649, September.
  49. Cindy Frascolla & Guillaume Lecuelle & Pascal Schlich & Hervé Cardot, 2022. "Two sample tests for Semi-Markov processes with parametric sojourn time distributions: an application in sensory analysis," Computational Statistics, Springer, vol. 37(5), pages 2553-2580, November.
  50. Fernández-de-Marcos, Alberto & García-Portugués, Eduardo, 2023. "Data-driven stabilizations of goodness-of-fit tests," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
  51. Juan C. Laria & M. Carmen Aguilera-Morillo & Rosa E. Lillo, 2023. "Group linear algorithm with sparse principal decomposition: a variable selection and clustering method for generalized linear models," Statistical Papers, Springer, vol. 64(1), pages 227-253, February.
  52. Verena Bauer & Karl Fürlinger & Göran Kauermann, 2019. "A note on parallel sampling in Markov graphs," Computational Statistics, Springer, vol. 34(3), pages 1087-1107, September.
  53. Julien Boelaert, 2013. "A Neural Network Demand System," Documents de travail du Centre d'Economie de la Sorbonne 13081, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  54. Oluwasegun Taiwo Ojo & Antonio Fernández Anta & Rosa E. Lillo & Carlo Sguera, 2022. "Detecting and classifying outliers in big functional data," 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. 16(3), pages 725-760, September.
  55. Matthew D. Koslovsky, 2023. "A Bayesian zero‐inflated Dirichlet‐multinomial regression model for multivariate compositional count data," Biometrics, The International Biometric Society, vol. 79(4), pages 3239-3251, December.
  56. Granados-Garcia, Guilllermo & Fiecas, Mark & Babak, Shahbaba & Fortin, Norbert J. & Ombao, Hernando, 2022. "Brain waves analysis via a non-parametric Bayesian mixture of autoregressive kernels," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
  57. Michael Braun & Paul Damien, 2016. "Scalable Rejection Sampling for Bayesian Hierarchical Models," Marketing Science, INFORMS, vol. 35(3), pages 427-444, May.
  58. Anoek Castelein & Dennis Fok & Richard Paap, 2019. "Dynamics in clickthrough and conversion probabilities of paid search advertisements," Tinbergen Institute Discussion Papers 19-056/III, Tinbergen Institute.
  59. Coube-Sisqueille, Sébastien & Liquet, Benoît, 2022. "Improving performances of MCMC for Nearest Neighbor Gaussian Process models with full data augmentation," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
  60. Hess, Stephane & Palma, David, 2019. "Apollo: A flexible, powerful and customisable freeware package for choice model estimation and application," Journal of choice modelling, Elsevier, vol. 32(C), pages 1-1.
  61. Sebastian Ankargren & Paulina Jon'eus, 2019. "Estimating Large Mixed-Frequency Bayesian VAR Models," Papers 1912.02231, arXiv.org.
  62. John O'Sullivan & Conor Sweeney & Andrew C. Parnell, 2020. "Bayesian spatial extreme value analysis of maximum temperatures in County Dublin, Ireland," Environmetrics, John Wiley & Sons, Ltd., vol. 31(5), August.
  63. Martinetti, Davide & Geniaux, Ghislain, 2017. "Approximate likelihood estimation of spatial probit models," Regional Science and Urban Economics, Elsevier, vol. 64(C), pages 30-45.
  64. Samrachana Adhikari & Tracy Sweet & Brian Junker, 2021. "Analysis of longitudinal advice‐seeking networks following implementation of high stakes testing," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1475-1500, October.
  65. Kahle, David J. & Young, Phil D. & Greer, Brandi A. & Young, Dean M., 2016. "Confidence intervals for the ratio of two Poisson rates under one-way differential misclassification using double sampling," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 122-132.
  66. Adam N. Smith & Jim E. Griffin, 2023. "Shrinkage priors for high-dimensional demand estimation," Quantitative Marketing and Economics (QME), Springer, vol. 21(1), pages 95-146, March.
  67. Francis J. DiTraglia, 2011. "Using Invalid Instruments on Purpose: Focused Moment Selection and Averaging for GMM, Second Version," PIER Working Paper Archive 14-045, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 09 Dec 2014.
  68. Francis DiTraglia, 2011. "Using Invalid Instruments on Purpose: Focused Moment Selection and Averaging for GMM, Second Version," PIER Working Paper Archive 15-027, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 10 Aug 2015.
  69. Guinness, Joseph, 2022. "Nonparametric spectral methods for multivariate spatial and spatial–temporal data," Journal of Multivariate Analysis, Elsevier, vol. 187(C).
  70. Bachoc, François & Genton, Mark G. & Nordhausen, Klaus & Ruiz-Gazen, Anne & Virta, Joni, 2019. "Spatial Blind Source Separation," TSE Working Papers 19-998, Toulouse School of Economics (TSE).
  71. Marchese, Scott & Diao, Guoqing, 2018. "Joint regression analysis of mixed-type outcome data via efficient scores," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 156-170.
  72. Bill Venables, 2017. "JOHN M. CHAMBERS . Extending R . Boca Raton : CRC Press," Biometrics, The International Biometric Society, vol. 73(2), pages 709-710, June.
  73. Di Mari, Roberto & Bakk, Zsuzsa & Oser, Jennifer & Kuha, Jouni, 2023. "A two-step estimator for multilevel latent class analysis with covariates," LSE Research Online Documents on Economics 119994, London School of Economics and Political Science, LSE Library.
  74. Jin, Shaobo & Moustaki, Irini & Yang-Wallentin, Fan, 2018. "Approximated penalized maximum likelihood for exploratory factor analysis: an orthogonal case," LSE Research Online Documents on Economics 88118, London School of Economics and Political Science, LSE Library.
  75. Dirk Eddelbuettel & James Joseph Balamuta, 2018. "Extending R with C++: A Brief Introduction to Rcpp," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 28-36, January.
  76. Carel F. W. Peeters & Mark A. Wiel & Wessel N. Wieringen, 2020. "The spectral condition number plot for regularization parameter evaluation," Computational Statistics, Springer, vol. 35(2), pages 629-646, June.
  77. Mazo, Gildas & Uyttendaele, Nathan, 2016. "Building conditionally dependent parametric one-factor copulas," LIDAM Discussion Papers ISBA 2016004, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
  78. Helmut Lutkepohl & Fei Shang & Luis Uzeda & Tomasz Wo'zniak, 2024. "Partial Identification of Heteroskedastic Structural VARs: Theory and Bayesian Inference," Papers 2404.11057, arXiv.org.
  79. Martina Sundqvist & Julien Chiquet & Guillem Rigaill, 2023. "Adjusting the adjusted Rand Index," Computational Statistics, Springer, vol. 38(1), pages 327-347, March.
  80. Martin Siebenborn & Julian Wagner, 2021. "A multigrid preconditioner for tensor product spline smoothing," Computational Statistics, Springer, vol. 36(4), pages 2379-2411, December.
  81. K. B. S. Huth & L. J. Waldorp & J. Luigjes & A. E. Goudriaan & R. J. Holst & M. Marsman, 2022. "A Note on the Structural Change Test in Highly Parameterized Psychometric Models," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 1064-1080, September.
  82. Steven Andrew Culpepper & Yinghan Chen, 2019. "Development and Application of an Exploratory Reduced Reparameterized Unified Model," Journal of Educational and Behavioral Statistics, , vol. 44(1), pages 3-24, February.
  83. Michael Platzer & Thomas Reutterer, 2016. "Ticking Away the Moments: Timing Regularity Helps to Better Predict Customer Activity," Marketing Science, INFORMS, vol. 35(5), pages 779-799, September.
  84. Battauz, Michela & Vidoni, Paolo, 2022. "A likelihood-based boosting algorithm for factor analysis models with binary data," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
  85. Bivand, Roger, 2011. "Geocomputation and open source software: components and software stacks," Discussion Paper Series in Economics 23/2011, Norwegian School of Economics, Department of Economics.
  86. Eddelbuettel, Dirk & Sanderson, Conrad, 2014. "RcppArmadillo: Accelerating R with high-performance C++ linear algebra," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1054-1063.
  87. Xiaotian Zhu & David R. Hunter, 2019. "Clustering via finite nonparametric ICA mixture models," 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 65-87, March.
  88. Masoudi, Ehsan & Holling, Heinz & Wong, Weng Kee, 2017. "Application of imperialist competitive algorithm to find minimax and standardized maximin optimal designs," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 330-345.
  89. Roger S. Bivand, 2021. "Progress in the R ecosystem for representing and handling spatial data," Journal of Geographical Systems, Springer, vol. 23(4), pages 515-546, October.
  90. Steenbeek, Jeroen & Buszowski, Joe & Christensen, Villy & Akoglu, Ekin & Aydin, Kerim & Ellis, Nick & Felinto, Dalai & Guitton, Jerome & Lucey, Sean & Kearney, Kelly & Mackinson, Steven & Pan, Mike & , 2016. "Ecopath with Ecosim as a model-building toolbox: Source code capabilities, extensions, and variations," Ecological Modelling, Elsevier, vol. 319(C), pages 178-189.
  91. J. Liu & Xinlian Zhang & T. Chen & T. Wu & T. Lin & L. Jiang & S. Lang & L. Liu & L. Natarajan & J.X. Tu & T. Kosciolek & J. Morton & T.T. Nguyen & B. Schnabl & R. Knight & C. Feng & Y. Zhong & X.M. T, 2022. "A semiparametric model for between‐subject attributes: Applications to beta‐diversity of microbiome data," Biometrics, The International Biometric Society, vol. 78(3), pages 950-962, September.
  92. Joshua L. Warren & Jiachen Cai & Nicholaus P. Johnson & Nicole C. Deziel, 2022. "A discrete kernel stick‐breaking model for detecting spatial boundaries in hydraulic fracturing wastewater disposal well placement across Ohio," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(1), pages 175-193, January.
  93. Erick da Conceição Amorim & Vinícius Diniz Mayrink, 2020. "Clustering non-linear interactions in factor analysis," METRON, Springer;Sapienza Università di Roma, vol. 78(3), pages 329-352, December.
  94. Semeyutin, Artur & O’Neill, Robert, 2019. "A brief survey on the choice of parameters for: “Kernel density estimation for time series data”," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
  95. Cédric Beaulac & Jeffrey S. Rosenthal, 2019. "Predicting University Students’ Academic Success and Major Using Random Forests," Research in Higher Education, Springer;Association for Institutional Research, vol. 60(7), pages 1048-1064, November.
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