Comparison of EM and SEM Algorithms in Poisson Regression Models: A Simulation Study

DOI10.1080/03610918.2011.594534zbMath1318.62111OpenAlexW2089491558MaRDI QIDQ4906429

Gilda Soromenho, Susana Faria

Publication date: 11 February 2013

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2011.594534

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Generalized linear models (logistic models) (62J12)

Related Items (4)

Comparison of the EM, CEM and SEM algorithms in the estimation of finite mixtures of linear mixed models: a simulation study ⋮ Censored count data regression with missing censoring information ⋮ Stochastic EM algorithm of a finite mixture model from hurdle Poisson distribution with missing responses ⋮ Variable selection in finite mixture of generalized estimating equations

Uses Software

• R

Cites Work

• Finite mixture and Markov switching models.
• Mixture Poisson regression models for heterogeneous count data based on latent and fuzzy class analysis
• On the Mixture of Distributions
• Mixed Poisson Regression Models with Covariate Dependent Rates
• Stochastic versions of the em algorithm: an experimental study in the mixture case
• Fitting mixtures of linear regressions

This page was built for publication: Comparison of EM and SEM Algorithms in Poisson Regression Models: A Simulation Study