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:: Volume 12, Issue 1 (9-2018) ::
J. of Stat. Sci. 2018, 12(1): 21-37 Back to browse issues page
A New Model Selection Criterion Based on Data Cloning
Sedighe Eshaghi , Hossein Baghishani , Negar Eghbal
Abstract:   (1236 Views)

Introducing some efficient model selection criteria for mixed models is a substantial challenge; Its source is indeed fitting the model and computing the maximum likelihood estimates of the parameters. Data cloning is a new method to fit mixed models efficiently in a likelihood-based approach. This method has been popular recently and avoids the main problems of other likelihood-based methods in mixed models. A disadvantage of data cloning is its inability of computing the maximum of likelihood function of the model. This value is a key quantity in proposing and calculating information criteria. Therefore, it seems that we can not, directly, define an appropriate information criterion by data cloning approach. In this paper, this believe is broken and a criterion based on data cloning is introduced. The performance of the proposed model selection criterion is also evaluated by a simulation study.

Keywords: MCMC algorithm, Generalized linear mixed model, Model selection criterion, Data cloning
Full-Text [PDF 192 kb]   (281 Downloads)    
Type of Study: Research | Subject: Statistical Inference
Received: 2016/06/17 | Accepted: 2018/04/15 | Published: 2018/04/15
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Eshaghi S, Baghishani H, Eghbal N. A New Model Selection Criterion Based on Data Cloning. J. of Stat. Sci.. 2018; 12 (1) :21-37
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Volume 12, Issue 1 (9-2018) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences
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