کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
4949191 1364221 2018 8 صفحه PDF ندارد دانلود رایگان
عنوان انگلیسی مقاله
A scalable and efficient covariate selection criterion for mixed effects regression models with unknown random effects structure
کلمات کلیدی
Akaike information criterion; Generalized linear mixed model; h-likelihood; Random coefficient model; Two-stage estimation; Variable selection;
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
A scalable and efficient covariate selection criterion for mixed effects regression models with unknown random effects structure
چکیده انگلیسی

A new model selection criterion for mixed effects regression models is introduced. The criterion is computable even when the model is fitted with a two-step method or when the structure and the distribution of the random effects are unknown. The criterion is especially useful in the early stage of the model building process when one needs to decide which covariates should be included in a mixed effects regression model, but has no knowledge of the random effect structure. This is particularly relevant in substantive fields where variable selection is guided by information criteria rather than regularization. The calculation of the criterion requires only the evaluation of cluster-level log-likelihoods and does not rely on heavy numerical integration. Theoretical and numerical arguments are used to justify the method and its usefulness is illustrated by analysing data from a youth behaviour study.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 117, January 2018, Pages 154-161
نویسندگان
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