کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
415887 681253 2011 17 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Modified versions of the Bayesian Information Criterion for sparse Generalized Linear Models
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
Modified versions of the Bayesian Information Criterion for sparse Generalized Linear Models
چکیده انگلیسی

The classical model selection criteria, such as the Bayesian Information Criterion (BIC) or Akaike information criterion (AIC), have a strong tendency to overestimate the number of regressors when the search is performed over a large number of potential explanatory variables. To handle the problem of the overestimation, several modifications of the BIC have been proposed. These versions rely on supplementing the original BIC with some prior distributions on the class of possible models. Three such modifications are presented and compared in the context of sparse Generalized Linear Models (GLMs). The related choices of priors are discussed and the conditions for the asymptotic equivalence of these criteria are provided. The performance of the modified versions of the BIC is illustrated with an extensive simulation study and a real data analysis. Also, simplified versions of the modified BIC, based on least squares regression, are investigated.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 55, Issue 11, 1 November 2011, Pages 2908–2924
نویسندگان
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