A Gibbs sampling scheme to the product partition model: an application to change-point problems
Computers and Operations Research
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Identifying Volatility Clusters Using the PPM: A Sensitivity Analysis
Computational Economics
A note on Bayesian identification of change points in data sequences
Computers and Operations Research
Short-term sales forecasting with change-point evaluation and pattern matching algorithms
Expert Systems with Applications: An International Journal
Text segmentation by product partition models and dynamic programming
Mathematical and Computer Modelling: An International Journal
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In this paper, we consider the product partition model for the estimation of normal means and variances of a sequence of observations that experiences changes in these parameters at unknown times. The estimates of the parameters by using product partition model are the weighted average of the estimates based in blocks (groups) of observations by the posterior relevance of these blocks which depends on the prior cohesions. We implement the Barry and Hartigan's method to this change point problem and propose an easy-to-implement modification to their method. We use Yao's prior cohesions and investigate the influence of different prior distributions to the parameter that indexes these cohesions in the product estimates. A comparison between the estimates obtained by using both these methods and those provided by using Yao's method is done considering different settings for its application. We apply the three methods presented in this paper to stock market data. The results seem to indicate that the method proposed is competitive and also that the prior specifications influence in the product estimates.