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Stepwise Signal Extraction via Marginal Likelihood.

Authors :
Du, Chao
Kao, Chu-Lan Michael
Kou, S. C.
Source :
Journal of the American Statistical Association; Mar2016, Vol. 111 Issue 513, p314-330, 17p
Publication Year :
2016

Abstract

This article studies the estimation of a stepwise signal. To determine the number and locations of change-points of the stepwise signal, we formulate a maximum marginal likelihood estimator, which can be computed with a quadratic cost using dynamic programming. We carry out an extensive investigation on the choice of the prior distribution and study the asymptotic properties of the maximum marginal likelihood estimator. We propose to treat each possible set of change-points equally and adopt an empirical Bayes approach to specify the prior distribution of segment parameters. A detailed simulation study is performed to compare the effectiveness of this method with other existing methods. We demonstrate our method on single-molecule enzyme reaction data and on DNA array comparative genomic hybridization (CGH) data. Our study shows that this method is applicable to a wide range of models and offers appealing results in practice. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01621459
Volume :
111
Issue :
513
Database :
Complementary Index
Journal :
Journal of the American Statistical Association
Publication Type :
Academic Journal
Accession number :
116170549
Full Text :
https://doi.org/10.1080/01621459.2015.1006365