Given a statistical functional T and a parametric family of
distributions, a bias reduced functional $\tilde{T}$ is defined by setting the
expected value of the statistic equal to the observed value. Under certain
regularity conditions this new statistic, called the target estimator, will
have smaller bias and mean square error than the original estimator. The
theoretical aspects are analyzed by using higher order von Mises expansions.
Several examples are given, including $M$-estimates of location and scale. The
procedure is applied to an autoregressive model, the errors-in-variables model
and the logistic regression model. A comparison with the jackknife and the
bootstrap estimators is also included.
Publié le : 1999-06-14
Classification:
Bias,
mean square error,
parametric family,
statistical functionals,
target estimates,
von Mises expansions,
62G99,
62E20
@article{1018031269,
author = {Cabrera, Javier and Fernholz, Luisa Turrin},
title = {Target estimation for bias and mean square error
reduction},
journal = {Ann. Statist.},
volume = {27},
number = {4},
year = {1999},
pages = { 1080-1104},
language = {en},
url = {http://dml.mathdoc.fr/item/1018031269}
}
Cabrera, Javier; Fernholz, Luisa Turrin. Target estimation for bias and mean square error
reduction. Ann. Statist., Tome 27 (1999) no. 4, pp. 1080-1104. http://gdmltest.u-ga.fr/item/1018031269/