ANALISIS BAYESIAN PADA REGRESI LOGISTIK MULTIVARIAT DENGAN ALGORITMA MCMC RANDOM WALK METROPOLIS - , ARIF MARJUKI ([Yogyakarta] : Universitas Gadjah Mada, 2013)
In many application, such as epidemiologic and biomedical studies, logistic regression is the standard approach for the analysis of binary and ordered categorical data. Common frequentist approaches, which can be used for data of this type, via generalized estimating equation (GEE, Zeger and Liang, 1986). Although the GEE approach solve this problem, the justification relies on large sample arguments. In this paper we follow a Bayesian approach to estimaste and inference, for multivariate binary and categorical data. Bayesian approach often produce a high complexity calculation and high dimensions integral. By using Markov chain Monte Carlo (MCMC) algorithms to obtain estimates of exact posterior distributions, there is no need to rely on large sample justifications. Itâs also fast and eficien in calculation. Bayesian methods involves the prior information of the parameters to estimate the posterior distribution. This article is motivated by the need to develop Bayesian methods for multivariate logistic regression, which allow simple noninformative prior distribution.