title: Stepwise Signal Extraction via Marginal Likelihood.
abstract: This paper studies the estimation of 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 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. 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 CGH data. Our study shows that this method is applicable to a wide range of models and offers appealing results in practice.
authors.affiliation: Statistics, University of Virginia, Charlottesville, VA 22904 ( cd2wb@virginia.edu ).
authors.forename: Chao
authors.initials: C
authors.lastname: Du
authors.affiliation: Research Center of Adaptive Data Analysis, National Central University, Taoyuan County 32001, Taiwan ( chulankao@gmail.com ).
authors.forename: Chu-Lan Michael
authors.initials: CM
authors.lastname: Kao
authors.affiliation: Statistics, Harvard University, Cambridge, MA, 02138 ( kou@stat.harvard.edu ).
authors.forename: S C
authors.initials: SC
authors.lastname: Kou
keywords: array CGH; asymptotic consistency; change-points; choice of prior; dynamic programming; marginal likelihood; model selection; single-molecule experiments