Bootstrap and Other Resampling Procedures

  • M. B. Rajarshi
Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)


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.


Markov Chain Sampling Distribution Consistent Estimator Bootstrap Procedure Edgeworth Expansion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© The Author(s) 2013

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of PunePuneIndia

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