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Multiple change-points detection by empirical Bayesian information criteria and Gibbs sampling induced stochastic search
- Source :
- Applied Mathematical Modelling. 72:202-216
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- Uncovering hidden change-points in an observed signal sequence is challenging both mathematically and computationally. We tackle this by developing an innovative methodology based on Markov chain Monte Carlo and statistical information theory. It consists of an empirical Bayesian information criterion (emBIC) to assess the fitness and virtue of candidate configurations of change-points, and a stochastic search algorithm induced from Gibbs sampling to find the optimal change-points configuration. Our emBIC is derived by treating the unknown change-point locations as latent data rather than parameters as is in traditional BIC, resulting in significant improvement over the latter which is known to mostly over-detect change-points. The use of the Gibbs sampler induced search enables one to quickly find the optimal change-points configuration with high probability and without going through computationally infeasible enumeration. We also integrate the Gibbs sampler induced search with a current BIC-based change-points sequential testing method, significantly improving the method’s performance and computing feasibility. We further develop two comprehensive 3-step computing procedures to implement the proposed methodology for practical use. Finally, simulation studies and real examples analyzing business and genetic data are presented to illustrate and assess the procedures.
- Subjects :
- Computer science
Applied Mathematics
Model selection
05 social sciences
Markov chain Monte Carlo
Information theory
01 natural sciences
010104 statistics & probability
symbols.namesake
Sequential analysis
Bayesian information criterion
Search algorithm
Modeling and Simulation
0502 economics and business
symbols
0101 mathematics
Algorithm
Selection (genetic algorithm)
050205 econometrics
Gibbs sampling
Subjects
Details
- ISSN :
- 0307904X
- Volume :
- 72
- Database :
- OpenAIRE
- Journal :
- Applied Mathematical Modelling
- Accession number :
- edsair.doi...........2a1aba570e080a4d83038746a4e43ecd