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Wayaning Apsari
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ESTIMASI PARAMETER REGRESI LOGISTIK MULTINOMIAL DENGAN METODE BAYES Wayaning Apsari; Hasbi Yasin; Sugito Sugito
Jurnal Gaussian Vol 2, No 1 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (567.108 KB) | DOI: 10.14710/j.gauss.v2i1.2746

Abstract

Multinomial logistic regression is a logistic regression where the dependent variable is polychotomous is dependent variable value of more than two categories. Multinomial logistic regression parameter estimation usually use classical method that is based only on current information obtained from the sample without taking into account the initial information of logistic regression parameters. If have early information  about parameter is prior distribution, the parameter estimation can use Bayes method. Bayesian methods combine information on the sample with prior distribution of information, and the results are expressed in the posterior distribution. If posterior distribution can not be derived analytically so approximated using Markov Chain Monte Carlo (MCMC) algorithm especially Metropolis-Hastings algorithm. This algorithm uses acceptance and rejection mechanism to generate a sequence of random samples. Keyword: Multinomial Logistic Regression, Bayes Method, Markov Chain Monte Carlo algorithm (MCMC), Metropolis-Hastings algorithm.
Co-Authors Hasbi Yasin Sugito Sugito