On Confidence Intervals from Simulation of Finite Markov Chains

Consider a finite state irreducible Markov reward chain. It is shown that there exist simulation estimates and confidence intervals for the expected first passage times and rewards as well as the expected average reward, with 100% coverage probability. The length of the confidence intervals converges to zero with probability one as the sample size increases; it also satisfies a large deviations property

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Year of publication:	1998
Authors:	Burnetas, Apostolos N. ; Katehakis, Michael N.
Publisher:	[S.l.] : SSRN
Subject:	Markov-Kette \| Markov chain \| Simulation \| Theorie \| Theory \| Statistische Methodenlehre \| Statistical theory
Description of contents:	Abstract [papers.ssrn.com]

Extent:	1 Online-Ressource
Type of publication:	Book / Working Paper
Language:	English
Notes:	In: Mathematical Methods Of Operations Research, Vol. 46, No. 2 (1997) Volltext nicht verfügbar
Classification:	C12 - Hypothesis Testing ; C15 - Statistical Simulation Methods; Monte Carlo Methods
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10014070165