Detecting points of change in time series
Computers and Operations Research
Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
An application of MCMC methods for the multiple change-points problem
Signal Processing - Special section on Markov Chain Monte Carlo (MCMC) methods for signal processing
A model-based approach to quality control of paper production: Research Articles
Applied Stochastic Models in Business and Industry - Innovative Statistical Models in the European Business and Industry
Exact and efficient Bayesian inference for multiple changepoint problems
Statistics and Computing
A statistical model of cluster stability
Pattern Recognition
Efficient Bayesian analysis of multiple changepoint models with dependence across segments
Statistics and Computing
Bayesian compressive sensing for cluster structured sparse signals
Signal Processing
Text segmentation by product partition models and dynamic programming
Mathematical and Computer Modelling: An International Journal
| Hi-index | 0.08 |
The identification of multiple clusters and/or change points is a problem encountered in many subject areas, ranging from machine learning, pattern recognition, genetics, criminality and disease mapping to finance and industrial control. We present a product partition model that, for the first time, includes dependence between clusters or segments. The across-cluster dependence is introduced into the model through the prior distributions of the parameters. We adopt a reversible jump Markov chain Monte Carlo (MCMC) algorithm to sample from the posterior distributions. We compare the partition model with across-cluster correlation to two other models previously introduced in the literature, which includes the original product partition model (PPM). These models assume independence among the clusters. We illustrate the use of the proposed model with three case studies and we perform a Monte Carlo study. We show that the inclusion of correlation between clusters is a competitive model for change-point identification. By accounting for this correlation, we achieve substantial improvements in the parameter estimates.