A likelihood ratio approach to sequential change point detection for a general class of parameters
Abstract
In this paper we propose a new approach for sequential monitoring of a parameter of a -dimensional time series, which can be estimated by approximately linear functionals of the empirical distribution function. We consider a closed-end-method, which is motivated by the likelihood ratio test principle and compare the new method with two alternative procedures. We also incorporate self-normalization such that estimation of the long-run variance is not necessary. We prove that for a large class of testing problems the new detection scheme has asymptotic level and is consistent. The asymptotic theory is illustrated for the important cases of monitoring a change in the mean, variance and correlation. By means of a simulation study it is demonstrated that the new test performs better than the currently available procedures for these problems.Finally the methodology is illustrated by a small data example investigating index prices from the dot-com bubble.
Cite
@article{arxiv.1802.07696,
title = {A likelihood ratio approach to sequential change point detection for a general class of parameters},
author = {Holger Dette and Josua Gösmann},
journal= {arXiv preprint arXiv:1802.07696},
year = {2018}
}