Balanced resampling for bootstrapping finite state Markov chains

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/27795

Title:	Balanced resampling for bootstrapping finite state Markov chains
Authors:	Fan,TH;Hung,WL
Contributors:	統計研究所
Keywords:	JACKKNIFE
Date:	1997
Issue Date:	2010-06-29 19:33:47 (UTC+8)
Publisher:	中央大學
Abstract:	In this paper, we study the Monte Carlo technique of balanced resampling for bootstrapping finite Markov chains. The balanced sampling method is a technique for improving the efficiency of Monte Carlo simulation. The objective here is to apply this idea to facilitate a reduction in the bootstrap replication size necessary to get approximate confidence intervals for the parameters of interest, such as transition probability and stationary distribution. The relative efficiency of bootstrap algorithm under uniform resampling with respect to balanced resampling is discussed. Some numerical results are also studied.
Relation:	COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

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