General saddlepoint approximation methods for smooth functions of M-estimates with bootstrap applications
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Bibliographic record
Abstract
Procedures are developed and implemented for computing general saddlepoint approximations for statistics defined by estimating equations and functions of these.Our approach is based on the fact that such statistics can be approximated as finite linear combinations of products of centered, normalized averages, and that cumulants of such approximants may be evaluated to any desired accuracy.The resulting approximations are useful in a wide variety of applications and may be computed using computer algebra routines.The application of these procedures to replace bootstrap sampling (in the case when the underlying distribution is an empirical cdf) is discussed.
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