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An Introduction to Bootstrap Methods with Applications to R by Robert A. LaBudde, Michael R. Chernick

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1

INTRODUCTION

1.1 HISTORICAL BACKGROUND

The “bootstrap” is one of a number of techniques that is now part of the broad umbrella of nonparametric statistics that are commonly called resampling methods. Some of the techniques are far older than the bootstrap. Permutation methods go back to Fisher (1935) and Pitman (1937, 1938), and the jackknife started with Quenouille (1949). Bootstrapping was made practical through the use of the Monte Carlo approximation, but it too goes back to the beginning of computers in the early 1940s.

However, 1979 is a critical year for the bootstrap because that is when Brad Efron's paper in the Annals of Statistics was published (Efron, 1979). Efron had defined a resampling procedure that he coined as bootstrap. He constructed it as a simple approximation to the jackknife (an earlier resampling method that was developed by John Tukey), and his original motivation was to derive properties of the bootstrap to better understand the jackknife. However, in many situations, the bootstrap is as good as or better than the jackknife as a resampling procedure. The jackknife is primarily useful for small samples, becoming computationally inefficient for larger samples but has become more feasible as computer speed increases. A clear description of the jackknife and its connecton to the bootstrap can be found in the SIAM monograph Efron (1982). A description of the jackknife is also given in Section 1.2.1.

Although permutation tests were known in the 1930s, an ...

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