ANALISIS BAYESIAN PADA REGRESI LOGISTIK MULTIVARIAT DENGAN ALGORITMA MCMC RANDOM WALK METROPOLIS
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,...
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| Main Authors: | , |
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| 格式: | Theses and Dissertations NonPeerReviewed |
| 出版: |
[Yogyakarta] : Universitas Gadjah Mada
2013
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| 主題: | |
| 在線閱讀: | https://repository.ugm.ac.id/124203/ http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=64323 |
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| 總結: | 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. It�s 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. |
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