UNS Conference Portal, The 1st International Conference on Science, Mathematics, Environment and Education 2017

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Parameter Estimation of Multivariate Regression Model using Bayesian with Prior Multivariate Normal and Inverse Wishart Distribution
Dina Ariek Prasdika

Last modified: 2017-07-19


The multivariate regression model is a linear regression model that has more than one dependent variables that correlates and some independent variables. Estimation of multivariate regression model parameters can use Bayesian method. In the Bayesian method two distributions are required, namely the prior distribution and posterior distribution. Generally, selection of prior distribution based on information about parameters is available or not. This article discusse about parameter estimation of multivariate multiple regression model with the information about parameters is available, so is used.  If the estimate of the multivariate regression paremeter is difficult to determine then a sample generation approaching posterior distribution with Markov chain Monte Carlo (MCMC) method is performed. The purpose of this study was to estimate multivariate regression model parameters using Bayesian method. The prior distribution used normal multivariat normal and inverse Wishart distribution.The posterior also used used normal multivariat normal and inverse Wishart distribution.The parameter estimation is done by determining the prior distribution and posterior distribution, then the application is done by simulation using Gibbs sampling algorithm