Bayesian analysis of robust Poisson geometric process model using heavy-tailed distributions

Wai-Yin Wan and Jennifer Chan

Computational Statistics & Data Analysis, 2011, vol. 55, issue 1, 687-702

Abstract: We propose a robust Poisson geometric process model with heavy-tailed distributions to cope with the problem of outliers as it may lead to an overestimation of mean and variance resulting in inaccurate interpretations of the situations. Two heavy-tailed distributions namely Student's t and exponential power distributions with different tailednesses and kurtoses are used and they are represented in scale mixture of normal and scale mixture of uniform respectively. The proposed model is capable of describing the trend and meanwhile the mixing parameters in the scale mixture representations can detect the outlying observations. Simulations and real data analysis are performed to investigate the properties of the models.

Keywords: Exponential; power; distribution; Geometric; process; Markov; chain; Monte; Carlo; algorithm; Mixture; effect; Outlier; diagnosis; Scale; mixture; representation (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:55:y:2011:i:1:p:687-702

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