Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

A Bayesian View on Detecting Drifts by Nonparametric Methods

A Bayesian View on Detecting Drifts by Nonparametric Methods Abstract We study a nonparametric sequential detection procedure, which aims at detecting the first time point where a drift term appears in a stationary process, from a Bayesian perspective. The approach is based on a nonparametric model for the drift, a nonparametric kernel smoother which is used to define the stopping rule, and a performance measure which determines for each smoothing kernel and each given drift the asymptotic accuracy of the method. We look at this approach by parameterizing the drift and putting a prior distribution on the parameter vector. We are able to identify the optimal prior distribution which minimizes the expected performance measure. Consequently, we can judge whether a certain prior distribution yields good or even optimal asymptotic detection. We consider several important special cases where the optimal prior can be calculated explicitly. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Economic Quality Control de Gruyter

A Bayesian View on Detecting Drifts by Nonparametric Methods

Economic Quality Control , Volume 17 (2) – Oct 1, 2002

Loading next page...
 
/lp/de-gruyter/a-bayesian-view-on-detecting-drifts-by-nonparametric-methods-yFV9rzY1o1
Publisher
de Gruyter
Copyright
Copyright © 2002 by the
ISSN
1869-6147
eISSN
1869-6147
DOI
10.1515/EQC.2002.177
Publisher site
See Article on Publisher Site

Abstract

Abstract We study a nonparametric sequential detection procedure, which aims at detecting the first time point where a drift term appears in a stationary process, from a Bayesian perspective. The approach is based on a nonparametric model for the drift, a nonparametric kernel smoother which is used to define the stopping rule, and a performance measure which determines for each smoothing kernel and each given drift the asymptotic accuracy of the method. We look at this approach by parameterizing the drift and putting a prior distribution on the parameter vector. We are able to identify the optimal prior distribution which minimizes the expected performance measure. Consequently, we can judge whether a certain prior distribution yields good or even optimal asymptotic detection. We consider several important special cases where the optimal prior can be calculated explicitly.

Journal

Economic Quality Controlde Gruyter

Published: Oct 1, 2002

There are no references for this article.