2015/09/02 by Xiaofei Wang, Wang, Xiaofei, John W. Emerson +1
Economics, Econometrics and Finance · Mathematics · #FOS: Computer and information sciences #Methodology (stat.ME) #Monetary Policy and Economic Impact #Spatial and Panel Data Analysis #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.1509.00817
openalex publication_date 2015/09/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Consider observations y1,…,yn on nodes of a connected graph, where the yi independently come from N(θi, σ2) distributions and an unknown partition divides the n observations into blocks. One well-studied class of change point problems assumes the means θi are equal for all nodes within contiguous blocks of a simple graph of sequential observations; both frequentist and Bayesian approaches have been used to estimate the θi and the change points of the underlying partition. This paper examines a broad class of change point problems on general connected graphs in which a regression model is assumed to apply within each block of the partition of the graph. This general class also supports multivariate change point problems. We use Bayesian methods to estimate change points or block boundaries of the underlying partition. This paper presents the methodology for the general class of change point problems and develops new algorithms for implementation via Markov Chain Monte Carlo. The paper concludes with simulations and real data examples to demonstrate application of the methodology on a wide range of problems.