Authors: Dette, Holger
Gösmann, Josua
Title: A likelihood ratio approach to sequential change point detection
Language (ISO): en
Abstract: In this paper we propose a new approach for sequential monitoring of a parameter of a d-dimensional time series. 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 longrun variance is not necessary. We prove that for a large class of testing problems the new detection scheme has asymptotic level a 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.
Subject Headings: change point analysis
likelihood ratio principle
sequential monitoring
Subject Headings (RSWK): Change-point-Problem
Sequenzieller Test
Issue Date: 2018
Appears in Collections:Sonderforschungsbereich (SFB) 823

Files in This Item:
File Description SizeFormat 
DP_0218_SFB823_Dette_Gösmann.pdfDNB568.69 kBAdobe PDFView/Open

This item is protected by original copyright

All resources in the repository are protected by copyright.