The goal of this paper is to develop a general framework for
constructing sequential fixed size confidence regions based on maximum
likelihood estimates. Asymptotic properties of the sequential procedure for
setting up the confidence regions are analyzed under very broad assumptions on
the underlying parametric model. It is shown that the proposed sequential
procedure is asymptotically optimal in the sense that it approximates the
optimal fixed-sample size procedure. It is further shown that the “cost
of ignorance” associated with the sequential procedure is bounded.
Applications are made to estimation problems arising in prospective and
retrospective studies.
@article{1015957403,
author = {Dmitrienko, A. and Govindarajulu, Z.},
title = {Sequential confidence regions for maximum likelihood
estimates},
journal = {Ann. Statist.},
volume = {28},
number = {3},
year = {2000},
pages = { 1472-1501},
language = {en},
url = {http://dml.mathdoc.fr/item/1015957403}
}
Dmitrienko, A.; Govindarajulu, Z. Sequential confidence regions for maximum likelihood
estimates. Ann. Statist., Tome 28 (2000) no. 3, pp. 1472-1501. http://gdmltest.u-ga.fr/item/1015957403/