275 citations · 619 across the 18 of their papers we have counts for
21 papers
Inference Optimal Long Run Variance Estimation with Lugsail Kernels
Rebecca P. Kurtz-Garcia, James M. Flegal
For datasets with unknown but stationary serial dependence, a robust long run variance estimator is essential to handle diverse scenarios. Spectral variance estimators are commonly…
Implementing MCMC: Multivariate estimation with confidence
James M. Flegal, Rebecca P. Kurtz-Garcia
This paper addresses the key challenge of estimating the asymptotic covariance associated with the Markov chain central limit theorem, which is essential for visualizing and termin…
Analyzing MCMC Output
Dootika Vats, Nathan Robertson, James M Flegal +1
Markov chain Monte Carlo (MCMC) is a sampling-based method for estimating features of probability distributions. MCMC methods produce a serially correlated, yet representative, sam…
Assessing and Visualizing Simultaneous Simulation Error
Nathan Robertson, James M. Flegal, Dootika Vats +1
Monte Carlo experiments produce samples in order to estimate features of a given distribution. However, simultaneous estimation of means and quantiles has received little attention…
Lugsail lag windows for estimating time-average covariance matrices
Dootika Vats, James M. Flegal
Lag windows are commonly used in time series, econometrics, steady-state simulation, and Markov chain Monte Carlo to estimate time-average covariance matrices. In the presence of p…
Weighted batch means estimators in Markov chain Monte Carlo
Ying Liu, James M. Flegal
This paper proposes a family of weighted batch means variance estimators, which are computationally efficient and can be conveniently applied in practice. The focus is on Markov ch…
Batch size selection for variance estimators in MCMC
Ying Liu, Dootika Vats, James M. Flegal
We consider batch size selection for a general class of multivariate batch means variance estimators, which are computationally viable for high-dimensional Markov chain Monte Carlo…
A Machine Learning Approach to Galaxy-LSS Classification I: Imprints on Halo Merger Trees
Jianan Hui, Miguel A. Aragon-Calvo, Xinping Cui +1
The cosmic web plays a major role in the formation and evolution of galaxies and defines, to a large extent, their properties. However, the relation between galaxies and environmen…
Multivariate Output Analysis for Markov chain Monte Carlo
Dootika Vats, James M. Flegal, Galin L. Jones
Markov chain Monte Carlo (MCMC) produces a correlated sample for estimating expectations with respect to a target distribution. A fundamental question is when should sampling stop…
Bayesian model selection on linear mixed-effects models for comparisons between multiple treatments and a control
Lei Gong, James M. Flegal, Stephen R. Spindler +1
We propose a novel Bayesian model selection technique on linear mixed-effects models to compare multiple treatments with a control. A fully Bayesian approach is implemented to esti…
Estimating standard errors for importance sampling estimators with multiple Markov chains
Vivekananda Roy, Aixin Tan, James M. Flegal
The naive importance sampling estimator, based on samples from a single importance density, can be numerically unstable. Instead, we consider generalized importance sampling estima…
Strong Consistency of Multivariate Spectral Variance Estimators
Dootika Vats, James M. Flegal, Galin L. Jones
Markov chain Monte Carlo (MCMC) algorithms are used to estimate features of interest of a distribution. The Monte Carlo error in estimation has an asymptotic normal distribution wh…
A practical sequential stopping rule for high-dimensional MCMC and its application to spatial-temporal Bayesian models
Lei Gong, James M. Flegal
A current challenge for many Bayesian analyses is determining when to terminate high-dimensional Markov chain Monte Carlo simulations. To this end, we propose using an automated se…
Bayesian inference for a flexible class of bivariate beta distributions
Roberto C. Crackel, James M. Flegal
Several bivariate beta distributions have been proposed in the literature. In particular, Olkin and Liu (2003) proposed a 3 parameter bivariate beta model, which Arnold and Ng (201…
A Modified Gibbs Sampler on General State Spaces
Alicia A. Johnson, James M. Flegal
We present a modified Gibbs sampler for general state spaces. We establish that this modification can lead to substantial gains in statistical efficiency while maintaining the over…
Relative fixed-width stopping rules for Markov chain Monte Carlo simulations
James M. Flegal, Lei Gong
Markov chain Monte Carlo (MCMC) simulations are commonly employed for estimating features of a target distribution, particularly for Bayesian inference. A fundamental challenge is…
Markov Chain Monte Carlo Estimation of Quantiles
Charles Doss, James M. Flegal, Galin L. Jones +1
We consider quantile estimation using Markov chain Monte Carlo and establish conditions under which the sampling distribution of the Monte Carlo error is approximately Normal. Furt…
Exact sampling for intractable probability distributions via a Bernoulli factory
James M. Flegal, Radu Herbei
Many applications in the field of statistics require Markov chain Monte Carlo methods. Determining appropriate starting values and run lengths can be both analytically and empirica…
Sweave Documentation for "Implementing Markov chain Monte Carlo: Estimating with confidence"
James M. Flegal, Galin L. Jones
This file is the Sweave documentation for the examples provided in Flegal, J. M. and Jones, G. L. (2010), "Implementing Markov chain Monte Carlo: Estimating with confidence", in Ha…
Batch means and spectral variance estimators in Markov chain Monte Carlo
James M. Flegal, Galin L. Jones
Calculating a Monte Carlo standard error (MCSE) is an important step in the statistical analysis of the simulation output obtained from a Markov chain Monte Carlo experiment. An MC…
Markov Chain Monte Carlo: Can We Trust the Third Significant Figure?
James M. Flegal, Murali Haran, Galin L. Jones
Current reporting of results based on Markov chain Monte Carlo computations could be improved. In particular, a measure of the accuracy of the resulting estimates is rarely reporte…