An EM algorithm for a mixture model of count data

doi:10.1016/0167-7152(93)90195-O

Statistics & Probability Letters

Volume 17, Issue 1, 3 May 1993, Pages 53-60

https://doi.org/10.1016/0167-7152(93)90195-O Get rights and content

Abstract

An EM-algorithm is proposed to study a mixture model for the analysis of count data. Iterative procedures for estimating the parameters are given for various discrete distributions including Binomial, Negative Binomial, and Poisson. The EM procedure is applied to a set of PVC data in Berry (1987).

References (7)

D.A. Berry
Logarithmic transformation in ANOVA
Biometrics
(1987)
A.P. Dempster et al.
Maximum likelihood from incomplete data via the EM algorithm (with discussion)
J. Roy. Statist. Soc. Ser. B
(1977)
V.T. Farewell et al.
The use of a mixture model in the analysis of count data
Biometrics
(1988)

There are more references available in the full text version of this article.

Cited by (10)

Quantifying the risk of mass shootings at specific locations
2024, Risk Analysis
Distance-based clustering of mixed data
2019, Wiley Interdisciplinary Reviews: Computational Statistics
Distance Metrics and Clustering Methods for Mixed-type Data
2019, International Statistical Review
Maximum Likelihood Estimation of Parameters in a Mixture Model
2016, Communications in Statistics: Simulation and Computation
Using zero inflated models to analyze dental caries with many zeroes
2010, Indian Journal of Dental Research
Zero-inflated models for regression analysis of count data: A study of growth and development
2002, Statistics in Medicine

View all citing articles on Scopus

View full text

An EM algorithm for a mixture model of count data

Abstract

Logarithmic transformation in ANOVA

Biometrics

Maximum likelihood from incomplete data via the EM algorithm (with discussion)

J. Roy. Statist. Soc. Ser. B

The use of a mixture model in the analysis of count data

Biometrics