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Bootstrap and Other Resampling Procedures

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Book cover Statistical Inference for Discrete Time Stochastic Processes

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Abstract

In this chapter, we discuss various resampling procedures, such as bootstrap, jackknife, and sample re-use procedures for discrete time stochastic processes. Our discussion begins with bootstrap procedures for finite and infinite Markov chains. Further, bootstrap for stationary real valued Markov sequences based on transition density estimators is discussed. This is followed by bootstrap based on residuals for stationary and invertible linear ARMA time series. We then describe bootstrap and jackknife procedures based on blocks of stationary observations. Further, we discuss a bootstrap procedure based on AR-sieves. Results which prove superiority of block-based bootstrap over the traditional central limit theorems are discussed herein. The last section discusses bootstrap procedures for construction of confidence intervals based on estimating functions.

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  1. Department of Statistics, University of Pune, University Road, Pune, Maharashtra, 411007, India

    M. B. Rajarshi

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Rajarshi, M.B. (2013). Bootstrap and Other Resampling Procedures. In: Statistical Inference for Discrete Time Stochastic Processes. SpringerBriefs in Statistics. Springer, India. https://doi.org/10.1007/978-81-322-0763-4_6

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