Information Technology Reference
In-Depth Information
SUMMARY
Desirable estimators are impartial, consistent, efficient, and robust, and
they have minimum loss. Interval estimates are to be preferred to point
estimates; they are less open to challenge for they convey information
about the estimate's precision.
TO LEARN MORE
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 6.
