Monte Carlo Simulation: Are We There Yet?

[1]  Galin L. Jones,et al.  On the applicability of regenerative simulation in Markov chain Monte Carlo , 2002 .

[2]  Murali Haran,et al.  Markov chain Monte Carlo: Can we trust the third significant figure? , 2007, math/0703746.

[3]  P. Glynn,et al.  The Asymptotic Validity of Sequential Stopping Rules for Stochastic Simulations , 1992 .

[4]  Galin L. Jones,et al.  Fixed-Width Output Analysis for Markov Chain Monte Carlo , 2006, math/0601446.

[5]  Brian S. Caffo,et al.  Empirical supremum rejection sampling , 2002 .

[6]  C. Geyer,et al.  Geometric Ergodicity of Gibbs and Block Gibbs Samplers for a Hierarchical Random Effects Model , 1998 .

[7]  James M. Flegal,et al.  Multivariate output analysis for Markov chain Monte Carlo , 2015, Biometrika.

[8]  Ning Dai,et al.  Multivariate initial sequence estimators in Markov chain Monte Carlo , 2017, J. Multivar. Anal..

[9]  Andrew F. Seila,et al.  Multivariate inference in stationary simulation using batch means , 1987, WSC '87.

[10]  Galin L. Jones.  On the Markov chain central limit theorem , 2004, math/0409112.

[11]  Andrew F. Seila,et al.  Multivariate estimation in regenerative simulation , 1982, Oper. Res. Lett..

[12]  James M. Flegal,et al.  Weighted batch means estimators in Markov chain Monte Carlo. , 2018, 1805.08283.

[13]  James M. Flegal,et al.  Lugsail lag windows for estimating time-average covariance matrices , 2021, Biometrika.

[14]  R. Tweedie,et al.  Geometric convergence and central limit theorems for multidimensional Hastings and Metropolis algorithms , 1996 .

[15]  David Goldsman,et al.  Large-Sample Results for Batch Means , 1997 .

[16]  D. Vats,et al.  Geometric Ergodicity of Gibbs Samplers in Bayesian Penalized Regression Models , 2016, 1609.04057.

[17]  Christian P. Robert,et al.  Accelerating MCMC algorithms , 2018, Wiley interdisciplinary reviews. Computational statistics.

[18]  James M. Flegal,et al.  Strong consistency of multivariate spectral variance estimators in Markov chain Monte Carlo , 2015, Bernoulli.

[19]  Jesse Frey,et al.  Fixed-Width Sequential Confidence Intervals for a Proportion , 2010 .

[20]  S. F. Jarner,et al.  Geometric ergodicity of Metropolis algorithms , 2000 .

[21]  James M. Flegal,et al.  Batch means and spectral variance estimators in Markov chain Monte Carlo , 2008, 0811.1729.

[22]  Kshitij Khare,et al.  Geometric ergodicity of the Bayesian lasso , 2013 .

[23]  Galin L. Jones,et al.  On the Geometric Ergodicity of Two-Variable Gibbs Samplers , 2012, 1206.4770.

[24]  Galin L. Jones,et al.  Sufficient burn-in for Gibbs samplers for a hierarchical random effects model , 2004, math/0406454.

[25]  D. Andrews.  Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation , 1991 .

[26]  J. Rosenthal,et al.  General state space Markov chains and MCMC algorithms , 2004, math/0404033.

[27]  James M. Flegal,et al.  Markov chain Monte Carlo estimation of quantiles , 2012, 1207.6432.

[28]  M. Kosorok.  Monte Carlo error estimation for multivariate Markov chains , 2000 .

[29]  Charles J. Geyer,et al.  Practical Markov Chain Monte Carlo , 1992 .

[30]  S. Haneuse,et al.  On the Assessment of Monte Carlo Error in Simulation-Based Statistical Analyses , 2009, The American statistician.

[31]  Acosta Archila,et al.  Markov Chain Monte Carlo for Linear Mixed Models , 2015 .

[32]  Brian P. Dawkins.  Siobhan's Problem: The Coupon Collector Revisited , 1991 .