Empirical Bayesian analysis of simultaneous changepoints in multiple data sequences
The Annals of Applied Statistics · 2017 · Volume 11 · Issue 4
Abstract
Copy number variations in cancer cells and volatility fluctuations in stock prices are commonly manifested as changepoints occurring at the same positions across related data sequences. We introduce a Bayesian modeling framework, BASIC, that employs a changepoint prior to capture the co-occurrence tendency in data of this type. We design efficient algorithms to sample from and maximize over the BASIC changepoint posterior and develop a Monte Carlo expectation-maximization procedure to select prior hyperparameters in an empirical Bayes fashion. We use the resulting BASIC framework to analyze DNA copy number variations in the NCI-60 cancer cell lines and to identify important events that affected the price volatility of S&P 500 stocks from 2000 to 2009.
Keywords
Changepoint detection, copy number variation, Empirical Bayes, Markov chain Monte Carlo, stock price volatility
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