A Hellinger distance approach to MCMC diagnostics

DOI10.1080/00949655.2012.729588zbMath1453.62050OpenAlexW2009575127WikidataQ57423944 ScholiaQ57423944MaRDI QIDQ5219943

J. R. W. Merrick, Matthew J. Krachey, Edward L. Boone

Publication date: 9 March 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2012.729588

zbMATH Keywords

Hellinger distanceMarkov chain Monte CarloBayesian robustnesskernel density estimation

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Density estimation (62G07) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (9)

Weighted batch means estimators in Markov chain Monte Carlo ⋮ Batch size selection for variance estimators in MCMC ⋮ MCMC diagnostics for higher dimensions using Kullback Leibler divergence ⋮ SOME NEW PROPERTIES OF HELLINGER DISTANCE FOR VALIDATING APPROXIMATIONS IN BAYESIAN ANALYSIS ⋮ Accelerating MCMC via Kriging-based adaptive independent proposals and delayed rejection ⋮ Structural differences in mixing behavior informing the role of asymptomatic infection and testing symptom heritability ⋮ Exact inference for a class of hidden Markov models on general state spaces ⋮ Hierarchical Bayesian LASSO for a negative binomial regression ⋮ Revisiting the Gelman-Rubin diagnostic

Uses Software

• dismo

Cites Work

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