Multivariate strong invariance principles in Markov chain Monte Carlo

Fuente: arXiv
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Main Authors: Banerjee, Arka, Vats, Dootika
Format: Preprint
Published: 2022
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author Banerjee, Arka
Vats, Dootika
author_facet Banerjee, Arka
Vats, Dootika
contents Strong invariance principles in Markov chain Monte Carlo are crucial to theoretically grounded output analysis. Using the wide-sense regenerative nature of the process, we obtain explicit bounds in the strong invariance converging rates for partial sums of multivariate ergodic Markov chains. Consequently, we present results on the existence of strong invariance principles for both polynomially and geometrically ergodic Markov chains without requiring a 1-step minorization condition. Our tight and explicit rates have a direct impact on output analysis, as it allows the verification of important conditions in the strong consistency of certain variance estimators.
format Preprint
id arxiv_https___arxiv_org_abs_2211_06855
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Multivariate strong invariance principles in Markov chain Monte Carlo
Banerjee, Arka
Vats, Dootika
Computation
Probability
Statistics Theory
Strong invariance principles in Markov chain Monte Carlo are crucial to theoretically grounded output analysis. Using the wide-sense regenerative nature of the process, we obtain explicit bounds in the strong invariance converging rates for partial sums of multivariate ergodic Markov chains. Consequently, we present results on the existence of strong invariance principles for both polynomially and geometrically ergodic Markov chains without requiring a 1-step minorization condition. Our tight and explicit rates have a direct impact on output analysis, as it allows the verification of important conditions in the strong consistency of certain variance estimators.
title Multivariate strong invariance principles in Markov chain Monte Carlo
topic Computation
Probability
Statistics Theory
url https://arxiv.org/abs/2211.06855