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Bootstrap Methods for Linear Models

In: Theory of Nonparametric Tests

Author

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  • Thorsten Dickhaus

    (University of Bremen, Institute for Statistics)

Abstract

We consider linear models with deterministic and random design, respectively. The finite-sample error distribution of the least squares estimator of the vector of regression coefficients is approximated by means of appropriate bootstrap methods. The (asymptotic) validity of these approximations is shown by means of (conditional) multivariate central limit theorems.

Suggested Citation

  • Thorsten Dickhaus, 2018. "Bootstrap Methods for Linear Models," Springer Books, in: Theory of Nonparametric Tests, chapter 0, pages 91-103, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-76315-6_6
    DOI: 10.1007/978-3-319-76315-6_6
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