کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10524889 957763 2012 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A model selection criterion for discriminant analysis of high-dimensional data with fewer observations
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات ریاضیات کاربردی
پیش نمایش صفحه اول مقاله
A model selection criterion for discriminant analysis of high-dimensional data with fewer observations
چکیده انگلیسی
This paper is concerned with the problem of selecting variables in two-group discriminant analysis for high-dimensional data with fewer observations than the dimension. We consider a selection criterion based on approximately unbiased for AIC type of risk. When the dimension is large compared to the sample size, AIC type of risk cannot be defined. We propose AIC by replacing maximum likelihood estimator with ridge-type estimator. This idea follows Srivastava and Kubokawa (2008). It has been further extended by Yamamura et al. (2010). Simulation revealed that the proposed AIC performs well.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Statistical Planning and Inference - Volume 142, Issue 12, December 2012, Pages 3134-3145
نویسندگان
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