Retrospective change-point detection and estimation in multivariate linear models

Details Retrospective change-point detection and estimation in multivariate linear models Retrospective change-point detection and estimation in multivariate linear models

2011-10-26

arxiv.org/abs/1110.5731v1

Mathematics

Statistics Theory

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Retrospective change-point detection and estimation in multivariate linear models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Retrospective change-point detection and estimation in multivariate linear models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Retrospective change-point detection and estimation in multivariate linear models will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-716968