A Computationally Rapid Penalized Likelihood Multiple Changepoint Procedure for Complex Model Structures

Thomas M. Menino Convention & Exhibition Center 

Multiple changepoint analyses have become an important tool in modern statistics. Classical approaches to the problem include dynamic programming, binary segmentation procedures and their variants, and windowed approaches. Fast dynamic programming procedures that optimize penalized likelihoods only apply when all model parameters change at each and every changepoint time, which is often physically unrealistic. Similarly, windowed approaches either assume that all dynamics change at each changepoint time, or that aspects that do not change at the changepoint time vary across windows. Penalized likelihood methods for the general case, where only a subset of parameters are allowed to change at the changepoint times, require extensive computational searches, classically done via genetic algorithms, to locate the optimal changepoint configuration.

This paper develops methods that rapidly estimate optimal penalized likelihood changepoint configurations in the general case, bypassing the slow computational (and stochastic) drawbacks of genetic algorithms. Consistency of the changepoint configuration and model parameters under infill asymptotics are proven; the procedure is shown to work well in finite samples via simulation. Applications to environmental and business problems are detailed.

Joint work with Colin Gallagher, Robert Lund, and Xueheng Shi.

segmentation

climate change

gradient descent

change point

environment

structural break