Statistical estimation methods for unconditional finite normal mixture distribution

Zetty Ain Kamaruzzaman, Zaidi Isa

Research output: Contribution to journalArticle

Abstract

Normal mixture distribution (NM) is arguably the most important mixture models, and also the most challenging technically. It has been successfully applied in many fields where the application is still expending. In this paper, we provide a tutorial exposition on expectation–maximization (EM) algorithm and Gibbs sampler for parameter estimation of unconditional finite Normal mixture distribution. Both methods are extremely useful for solving difficult computation problems especially in Normal mixture distribution case. Practical issues that arise in the use of EM algorithm and Gibb sampler are discussed, as well as variants of algorithm and programming that help to deal with these challenges. The purpose of this paper is to provide a good conceptual explanation of the statistical estimation methods with illustrative example so the reader can have a grasp of some of the basic principles and techniques.

Original languageEnglish
Pages (from-to)3687-3701
Number of pages15
JournalGlobal Journal of Pure and Applied Mathematics
Volume11
Issue number5
Publication statusPublished - 2015

Fingerprint

Normal Mixture
Mixture Distribution
Statistical Estimation
Finite Mixture
Gaussian distribution
Expectation-maximization Algorithm
Gibbs Sampler
Mixture Model
Parameter Estimation
Programming
Parameter estimation

Keywords

  • EM algorithm
  • Gibbs sampler
  • Normal mixture distribution

ASJC Scopus subject areas

  • Mathematics(all)
  • Applied Mathematics

Cite this

Statistical estimation methods for unconditional finite normal mixture distribution. / Kamaruzzaman, Zetty Ain; Isa, Zaidi.

In: Global Journal of Pure and Applied Mathematics, Vol. 11, No. 5, 2015, p. 3687-3701.

Research output: Contribution to journalArticle

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