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Selecting more informative endpoints is the focus of Berger [2002] and Bland and Altman [1995].
Lehmann and Casella [1998] provide a detailed theory of point estimation.
Robust estimators are considered in Huber [1981], Maritz [1996], and Bickel et al. [1993]. Additional examples of both parametric and nonparametric bootstrap estimation procedures may be found in Efron and Tibshirani [1993]. Shao and Tu [1995; Section 4.4] provide a more extensive review of bootstrap estimation methods along with a summary of empirical comparisons.
Carroll and Ruppert [2000] show how to account for differences in variances between populations; this is a necessary step if one wants to take advantage of Stein–James–Efron–Morris estimators.
Bayes estimators are considered in Chapter 7.
Notes
1 See, for example, Yoo [2001].
2 It is also true in some cases for very large samples. How large the sample must be in each case will depend both upon the parameter being estimated and the distribution from which the observations are drawn.
3 Stata™ provides for bias-corrected intervals via its bstrap command. R and S-Plus both include BCa functions. A SAS macro is available at http://cuke.hort.ncsu.edu/cucurbit/wehner/software/pathsas/jackboot.txt.
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