文章基本信息

标题：A new robust model selection method in GLM with application to ecological data
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作者：D. M. Sakate ; D. M. Sakate ; D. N. Kashid 等
期刊名称：Environmental Systems Research
电子版ISSN：2193-2697
出版年度：2016
卷号：5
期号：1
页码：1-8
DOI：10.1186/s40068-016-0060-7
语种：English
出版社：Springer
摘要：Abstract Background Generalized linear models (GLM) are widely used to model social, medical and ecological data. Choosing predictors for building a good GLM is a widely studied problem. Likelihood based procedures like Akaike Information criterion and Bayes Information Criterion are usually used for model selection in GLM. The non-robustness property of likelihood based procedures in the presence of outliers or deviation from assumed distribution of response is widely studied in the literature. Results The deviance based criterion (DBC) is modified to define a robust and consistent model selection criterion called robust deviance based criterion (RDBC). Further, bootstrap version of RDBC is also proposed. A simulation study is performed to compare proposed model selection criterion with the existing one. It indicates that the performance of proposed criteria is compatible with the existing one. A key advantage of the proposed criterion is that it is very simple to compute. Conclusions The proposed model selection criterion is applied to arboreal marsupials data and model selection is carried out. The proposed criterion can be applied to data from any discipline mitigating the effect of outliers or deviation from the assumption of distribution of response. It can be implemented in any statistical software. In this article, R software is used for the computations.
关键词：KeywordsEnArboreal marsupialsBootstrapDBCRobust estimation