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
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات کاربردی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: A model selection criterion for discriminant analysis of high-dimensional data with fewer observations A model selection criterion for discriminant analysis of high-dimensional data with fewer observations](/preview/png/10524889.png)
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
Journal: Journal of Statistical Planning and Inference - Volume 142, Issue 12, December 2012, Pages 3134-3145
نویسندگان
Masashi Hyodo, Takayuki Yamada, Muni S. Srivastava,