Observations are generated according to a regression with normal
error as a function of time,when the process is in control. The process
potentially changes at some unknown point oftime and then the ensuing
observations are normal with the same mean function plus an arbitrary function
under suitable regularity conditions. The problem is to obtain a stopping rule
that is optimal in the sense that the rule minimizes the expected delay in
detecting a change subject to a constraint on the average run length to a false
alarm. A bound on the expected delay is first obtained. It is then shown that
the cusum and Shiryayev–Roberts procedures achieve this bound to first
order.
Publié le : 1999-12-14
Classification:
Change point detection,
regression,
stopping rules,
information bound,
62L10,
62N10
@article{1017939243,
author = {Yakir, Benjamin and Krieger, Abba M. and Pollak, Moshe},
title = {Detecting a change in regression: first-order optimality},
journal = {Ann. Statist.},
volume = {27},
number = {4},
year = {1999},
pages = { 1896-1913},
language = {en},
url = {http://dml.mathdoc.fr/item/1017939243}
}
Yakir, Benjamin; Krieger, Abba M.; Pollak, Moshe. Detecting a change in regression: first-order optimality. Ann. Statist., Tome 27 (1999) no. 4, pp. 1896-1913. http://gdmltest.u-ga.fr/item/1017939243/