Stepwise Signal Extraction via Marginal Likelihood

• 成果类型: Article
• 署名作者: Du, Chao; Kao, Chu-Lan Michael; Kou, S. C.
• 署名单位: University of Virginia; National Central University; Harvard University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2015.1006365
• 发表日期: 2016
• 页码: 314-330
• 关键词: multiple change-point; circular binary segmentation; single-molecule spectroscopy; dna-sequence segmentation; hidden markov-models; array cgh data; changepoint problems; nanoscale biophysics; enzymatic-reaction; bayesian-analysis

摘要

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.