Stochastic EM algorithm of a finite mixture model from hurdle Poisson distribution with missing responses
DOI10.1080/03610926.2014.953689zbMath1396.62132OpenAlexW2268165647MaRDI QIDQ2830181
Publication date: 9 November 2016
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2014.953689
model selectionBayesian information criterionfinite mixture modelmissing datastochastic EM algorithmhurdle Poisson distribution
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Generalized linear models (logistic models) (62J12)
Cites Work
- Mixtures of power series distributions: identifiability via uniqueness in problems of moments
- On modeling claim frequency data in general insurance with extra zeros
- Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable
- Variable Selection with Incomplete Covariate Data
- Zero-Inflated Poisson Models and C.A.MAN: A Tutorial Collection of Evidence
- Comparison of EM and SEM Algorithms in Poisson Regression Models: A Simulation Study
- Model Selection Criteria for Missing-Data Problems Using the EM Algorithm
- Semiparametric Models for Missing Covariate and Response Data in Regression Models
- A Semiparametric Mixture Model for Analyzing Clustered Competing Risks Data
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