Dette, HolgerGösmann, Josua2018-02-282018-02-282018http://hdl.handle.net/2003/3678210.17877/DE290R-18783In 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.enDiscussion Paper / SFB823;2/2018change point analysislikelihood ratio principlesequential monitoringself-normalization310330620working paperChange-point-ProblemSequenzieller TestLikelihood-Quotienten-Test