Retrospective change-point detection and estimation in multivariate linear models
Abstract
In this paper the problem of retrospective change-point detection and estimation in multivariate linear models is considered. The lower bounds for the error of change-point estimation are proved in different cases (one change-point: deterministic and stochastic predictors, multiple change-points). A new method for retrospective change-point detection and estimation is proposed and its main performance characteristics (type 1 and type 2 errors, the error of estimation) are studied for dependent observations in situations of deterministic and stochastic predictors and unknown change-points. We prove that this method is asymptotically optimal by the order of convergence of change-point estimates to their true values as the sample size tends to infinity. Results of a simulation study of the main performance characteristics of proposed method in comparison with other well known methods of retrospective change-point detection and estimation are presented.
Cite
@article{arxiv.1110.5731,
title = {Retrospective change-point detection and estimation in multivariate linear models},
author = {Boris Brodsky and Boris Darkhovsky},
journal= {arXiv preprint arXiv:1110.5731},
year = {2011}
}