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Extensions and Examples

  • S. N. Lahiri
Part of the Springer Series in Statistics book series (SSS)

Abstract

In this chapter, we establish consistency of different block bootstrap methods for some general classes of estimators and consider some specific examples illustrating the theoretical results. Section 4.2 establishes consistency of estimators that may be represented as smooth functions of sample means. Section 4.3 deals with (generalized) M-estimators, including the maximum likelihood estimators of parameters, which are defined through estimating equations. Some special considerations are required while defining the bootstrap versions of such estimators. We describe the relevant issues in detail in Section 4.3. Section 4.4 gives results on the bootstrapped empirical process, and establishes consistency of bootstrap estimators for certain differentiable statistical functionals. Section 4.5 contains three numerical examples, illustrating the theoretical results of Sections 4.2–4.4.

Keywords

Empirical Process Block Bootstrap Discrete Uniform Distribution Bootstrap Estimator Bootstrap Version 
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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Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • S. N. Lahiri
    • 1
  1. 1.Department of StatisticsIowa State UniversityAmesUSA

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