BibTex Citation Data :
@article{J.Gauss2746, author = {Wayaning Apsari and Hasbi Yasin and Sugito Sugito}, title = {ESTIMASI PARAMETER REGRESI LOGISTIK MULTINOMIAL DENGAN METODE BAYES}, journal = {Jurnal Gaussian}, volume = {2}, number = {1}, year = {2013}, keywords = {Multinomial Logistic Regression, Bayes Method, Markov Chain Monte Carlo algorithm (MCMC), Metropolis-Hastings algorithm.}, 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. }, issn = {2339-2541}, pages = {79--88} doi = {10.14710/j.gauss.2.1.79-88}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/2746} }
Refworks Citation Data :
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.
Article Metrics:
Last update:
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to Media Statistika journal and Department of Statistics, Universitas Diponegoro as the publisher of the journal. Copyright encompasses the rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations.
Jurnal Gaussian and Department of Statistics, Universitas Diponegoro and the Editors make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in Jurnal Gaussian journal are the sole and exclusive responsibility of their respective authors and advertisers.
The Copyright Transfer Form can be downloaded here: [Copyright Transfer Form Jurnal Gaussian]. The copyright form should be signed originally and send to the Editorial Office in the form of original mail, scanned document or fax :
Dr. Rukun Santoso (Editor-in-Chief) Editorial Office of Jurnal GaussianDepartment of Statistics, Universitas DiponegoroJl. Prof. Soedarto, Kampus Undip Tembalang, Semarang, Central Java, Indonesia 50275Telp./Fax: +62-24-7474754Email: jurnalgaussian@gmail.com
Jurnal Gaussian by Departemen Statistika Undip is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Visitor Number:
View statistics
