Empirical Bayesian analysis of simultaneous changepoints in multiple data sequences

TL;DR

This paper introduces BASIC, a Bayesian framework for detecting simultaneous changepoints across multiple data sequences, with applications in cancer genomics and financial market analysis.

Contribution

It develops a novel Bayesian model and efficient algorithms for joint changepoint detection, including an empirical Bayes hyperparameter selection method.

Findings

Identified key copy number variation events in cancer cell lines.

Detected significant volatility shifts in S&P 500 stocks.

Demonstrated improved detection of co-occurring changepoints.

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.