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• W2029707941 abstract "The block bootstrap is the best known bootstrap method for time-series data when the analyst does not have a parametric model that reduces the data generation process to simple random sampling. However, the errors made by the block bootstrap converge to zero only slightly faster than those made by first-order asymptotic approximations. This paper describes a bootstrap procedure for data that are generated by a Markov process or a process that can be approximated by a Markov process with sufficient accuracy. The procedure is based on estimating the Markov transition density nonparametrically. Bootstrap samples are obtained by sampling the process implied by the estimated transition density. Conditions are given under which the errors made by the Markov bootstrap converge to zero more rapidly than those made by the block bootstrap." @default.
• W2029707941 created "2016-06-24" @default.
• W2029707941 creator A5061745932 @default.
• W2029707941 date "2003-07-01" @default.
• W2029707941 modified "2023-10-02" @default.
• W2029707941 title "Bootstrap Methods for Markov Processes" @default.
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• W2029707941 doi "https://doi.org/10.1111/1468-0262.00439" @default.
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