Robust Retrospective Multiple Change-point Estimation for Multivariate Data

Details Robust Retrospective Multiple Change-point Estimation for Multivariate Data Robust Retrospective Multiple Change-point Estimation for Multivariate Data

2011-02-09

arxiv.org/abs/1102.1796v2

Statistics

Methodology

submitted to IEEE Workshop on Statistical Signal Processing 2011

Scientific paper

We propose a non-parametric statistical procedure for detecting multiple change-points in multidimensional signals. The method is based on a test statistic that generalizes the well-known Kruskal-Wallis procedure to the multivariate setting. The proposed approach does not require any knowledge about the distribution of the observations and is parameter-free. It is computationally efficient thanks to the use of dynamic programming and can also be applied when the number of change-points is unknown. The method is shown through simulations to be more robust than alternatives, particularly when faced with atypical distributions (e.g., with outliers), high noise levels and/or high-dimensional data.

Affiliated with

Also associated with

No associations

Rating

Robust Retrospective Multiple Change-point Estimation for Multivariate Data 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 Robust Retrospective Multiple Change-point Estimation for Multivariate Data, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Robust Retrospective Multiple Change-point Estimation for Multivariate Data will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-649944