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Fixed-Width Output Analysis for Markov Chain Monte Carlo

Citations

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Cited by:

  1. Terrance Savitsky & Daniel McCaffrey, 2014. "Bayesian Hierarchical Multivariate Formulation with Factor Analysis for Nested Ordinal Data," Psychometrika, Springer;The Psychometric Society, vol. 79(2), pages 275-302, April.
  2. Jung, Yeun Ji & Hobert, James P., 2014. "Spectral properties of MCMC algorithms for Bayesian linear regression with generalized hyperbolic errors," Statistics & Probability Letters, Elsevier, vol. 95(C), pages 92-100.
  3. Bhattacharya, Sourabh, 2008. "Consistent estimation of the accuracy of importance sampling using regenerative simulation," Statistics & Probability Letters, Elsevier, vol. 78(15), pages 2522-2527, October.
  4. Chen Ray-Bing & Lee Kuo-Jung & Chen Yi-Chi & Chu Chi-Hsiang, 2017. "On the determinants of the 2008 financial crisis: a Bayesian approach to the selection of groups and variables," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(5), pages 1-17, December.
  5. Zheng, Wei & Jin, Yong & Zhang, Guoyi, 2016. "Recursive estimation of time-average variance constants through prewhitening," Statistics & Probability Letters, Elsevier, vol. 114(C), pages 30-37.
  6. Susan M. Paddock & Terrance D. Savitsky, 2013. "Bayesian hierarchical semiparametric modelling of longitudinal post-treatment outcomes from open enrolment therapy groups," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(3), pages 795-808, June.
  7. Joshua Chan & Arnaud Doucet & Roberto Leon-Gonzalez & Rodney W. Strachan, 2018. "Multivariate Stochastic Volatility with Co-Heteroscedasticity," GRIPS Discussion Papers 18-12, National Graduate Institute for Policy Studies.
  8. 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).
  9. Khare, Kshitij & Hobert, James P., 2012. "Geometric ergodicity of the Gibbs sampler for Bayesian quantile regression," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 108-116.
  10. Yu Hang Jiang & Tong Liu & Zhiya Lou & Jeffrey S. Rosenthal & Shanshan Shangguan & Fei Wang & Zixuan Wu, 2022. "Markov Chain Confidence Intervals and Biases," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 11(1), pages 1-29, March.
  11. Peter L. Boveng & Jay M. Ver Hoef & David E. Withrow & Josh M. London, 2018. "A Bayesian Analysis of Abundance, Trend, and Population Viability for Harbor Seals in Iliamna Lake, Alaska," Risk Analysis, John Wiley & Sons, vol. 38(9), pages 1988-2009, September.
  12. Roger D. Peng & Francesca Dominici & Leah J. Welty, 2009. "A Bayesian hierarchical distributed lag model for estimating the time course of risk of hospitalization associated with particulate matter air pollution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(1), pages 3-24, February.
  13. Alex Stivala & Garry Robins & Alessandro Lomi, 2020. "Exponential random graph model parameter estimation for very large directed networks," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-21, January.
  14. Krzysztof Łatuszyński & Gareth O. Roberts, 2013. "CLTs and Asymptotic Variance of Time-Sampled Markov Chains," Methodology and Computing in Applied Probability, Springer, vol. 15(1), pages 237-247, March.
  15. James C. Russell & Ephraim M. Hanks & Andreas P. Modlmeier & David P. Hughes, 2017. "Modeling Collective Animal Movement Through Interactions in Behavioral States," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 313-334, September.
  16. Li, Yong & Yu, Jun & Zeng, Tao, 2018. "Specification tests based on MCMC output," Journal of Econometrics, Elsevier, vol. 207(1), pages 237-260.
  17. Johnson, Alicia A. & Jones, Galin L., 2015. "Geometric ergodicity of random scan Gibbs samplers for hierarchical one-way random effects models," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 325-342.
  18. Jian, Zhihong & Li, Xupei & Zhu, Zhican, 2020. "Sequential forecasting of downside extreme risk during overnight and daytime: Evidence from the Chinese Stock Market☆," Pacific-Basin Finance Journal, Elsevier, vol. 64(C).
  19. White, Staci A. & Herbei, Radu, 2015. "A Monte Carlo approach to quantifying model error in Bayesian parameter estimation," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 168-181.
  20. Chakraborty, Saptarshi & Bhattacharya, Suman K. & Khare, Kshitij, 2022. "Estimating accuracy of the MCMC variance estimator: Asymptotic normality for batch means estimators," Statistics & Probability Letters, Elsevier, vol. 183(C).
  21. Choi, Hee Min & Hobert, James P., 2013. "Analysis of MCMC algorithms for Bayesian linear regression with Laplace errors," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 32-40.
  22. Sierra Pugh & Matthew J. Heaton & Jeff Svedin & Neil Hansen, 2019. "Spatiotemporal Lagged Models for Variable Rate Irrigation in Agriculture," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(4), pages 634-650, December.
  23. Roy, Vivekananda & Hobert, James P., 2010. "On Monte Carlo methods for Bayesian multivariate regression models with heavy-tailed errors," Journal of Multivariate Analysis, Elsevier, vol. 101(5), pages 1190-1202, May.
  24. Takaaki Koike & Marius Hofert, 2019. "Markov Chain Monte Carlo Methods for Estimating Systemic Risk Allocations," Papers 1909.11794, arXiv.org, revised May 2020.
  25. Dai, Ning & Jones, Galin L., 2017. "Multivariate initial sequence estimators in Markov chain Monte Carlo," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 184-199.
  26. Joshua Goldstein & Murali Haran & Ivan Simeonov & John Fricks & Francesca Chiaromonte, 2015. "An attraction–repulsion point process model for respiratory syncytial virus infections," Biometrics, The International Biometric Society, vol. 71(2), pages 376-385, June.
  27. Takaaki Koike & Mihoko Minami, 2017. "Estimation of Risk Contributions with MCMC," Papers 1702.03098, arXiv.org, revised Jan 2019.
  28. Zhengyi Zhou & David S. Matteson & Dawn B. Woodard & Shane G. Henderson & Athanasios C. Micheas, 2015. "A Spatio-Temporal Point Process Model for Ambulance Demand," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 6-15, March.
  29. Takaaki Koike & Marius Hofert, 2020. "Markov Chain Monte Carlo Methods for Estimating Systemic Risk Allocations," Risks, MDPI, vol. 8(1), pages 1-33, January.
  30. Ying Liu & Dootika Vats & James M. Flegal, 2022. "Batch Size Selection for Variance Estimators in MCMC," Methodology and Computing in Applied Probability, Springer, vol. 24(1), pages 65-93, March.
  31. Masahiro Kuroda & Hiroki Hashiguchi & Shigekazu Nakagawa & Zhi Geng, 2013. "MCMC using Markov bases for computing $$p$$ -values in decomposable log-linear models," Computational Statistics, Springer, vol. 28(2), pages 831-850, April.
  32. James C. Russell & Ephraim M. Hanks & Murali Haran, 2016. "Dynamic Models of Animal Movement with Spatial Point Process Interactions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(1), pages 22-40, March.
  33. Kin Wai Chan & Chun Yip Yau, 2017. "High-order Corrected Estimator of Asymptotic Variance with Optimal Bandwidth," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(4), pages 866-898, December.
  34. Trevezas, S. & Malefaki, S. & Cournède, P.-H., 2014. "Parameter estimation via stochastic variants of the ECM algorithm with applications to plant growth modeling," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 82-99.
  35. Won Chang & Murali Haran & Patrick Applegate & David Pollard, 2016. "Calibrating an Ice Sheet Model Using High-Dimensional Binary Spatial Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 57-72, March.
  36. Vitoratou, Silia & Ntzoufras, Ioannis & Moustaki, Irini, 2016. "Explaining the behavior of joint and marginal Monte Carlo estimators in latent variable models with independence assumptions," LSE Research Online Documents on Economics 57685, London School of Economics and Political Science, LSE Library.
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