An Application of the Bayesian Model Selection By Using Bayes Factor, Bayesian Informatıon Criterion And Deviance Information Criterion

Year 2013, Volume: 10 Issue: 2, 25 - 41, 15.07.2013

Mutlu Kaya Emel Çankaya

Abstract

In statistical modelling studies, due to the advanced technology and methodological developments, it is possible to construct alternative models assumed to generate the data. Therefore, the process of choosing “the best model” among available competing models appears to be one of the crucial steps that has to be included in the modelling process. In this study, Bayes factor, which is a preferred Bayesian approach to the solution of statistical model selection problem, is introduced. For the cases when analytical computation of Bayes factor is not possible, in addition to Bayesian Information Criterion (BIC), Carlin and Chib method based on Markov Chain Monte Carlo (MCMC) simulation is explained. Besides, a frequently used criteria in the recent years of model selection applications, namely Deviance Information Criterion (DIC), which has a completely different working principle than Bayes factor, is described in detail. Two models appeared in the literature as a result of an application of quantal modelling, which is an example of a semi-parametric modelling, are compared by means of Bayes factor, BIC and DIC.

Keywords

Bayes factor, Carlin and Chib method, DIC, MCMC, BIC

Bayes Faktörü, Bayesci Bilgi Ölçütü ve Sapma Bilgi Ölçütü Kullanımıyla Bayesci Model Seçiminin Bir Uygulaması

Abstract

İstatistiksel modelleme çalışmalarında, artan ileri teknoloji ve metodolojik gelişmeler sayesinde veriyi ürettiği varsayılan alternatif modeller oluşturabilmek mümkün olmaktadır. Dolayısıyla, mevcut rakip modeller arasından “en iyi” olanı seçme işlemi, modelleme sürecine dahil edilmesi gereken önemli aşamalardan biri olarak ortaya çıkmaktadır. Bu çalışmada, istatistiksel model seçimi probleminin Bayesci yaklaşımla çözümünde tercih edilen Bayes faktörü tanıtılmış, analitik olarak hesaplanmasının mümkün olmadığı durumlarda kullanılabilen Bayesci Bilgi Ölçütü (BIC) yanı sıra Markov Zincir Monte Carlo (MCMC) simülasyonuna dayalı Carlin ve Chib yöntemi açıklanmıştır. Ayrıca Bayes faktöründen tamamen farklı prensipte çalışan ve son yıllarda model seçimi uygulamalarında sıklıkla kullanılan Sapma Bilgi Ölçütü (DIC) ayrıntılı olarak anlatılmıştır. Bir yarı-parametrik modelleme örneği olan kuantal modellemenin, literatürdeki bir uygulaması sonucu ortaya çıkan alternatif iki model Bayes faktörü, BIC ve DIC kullanılarak kıyaslanmıştır.

Keywords

Bayes faktörü, Carlin ve Chib yöntemi, DIC, MCMC, BIC

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