On Dynamic Generalized Linear Models with Applications

Sourish Das () and Dipak K. Dey ()
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Sourish Das: SAS Research and Development
Dipak K. Dey: University of Connecticut

Methodology and Computing in Applied Probability, 2013, vol. 15, issue 2, 407-421

Abstract: Abstract In this paper we combine the idea of ‘power steady model’, ‘discount factor’ and ‘power prior’, for a general class of filter model, more specifically within a class of dynamic generalized linear models (DGLM). We show an optimality property for our proposed method and present the particle filter algorithm for DGLM as an alternative to Markov chain Monte Carlo method. We also present two applications; one on dynamic Poisson models for hurricane count data in Atlantic ocean and the another on the dynamic Poisson regression model for longitudinal count data.

Keywords: Count data; Dynamic model; Information processing; Kalman filter; Particle filter; Poisson models; 62P10; 62P12; 62P20 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11009-011-9255-6

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