کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1146548 | 957517 | 2010 | 20 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Statistical inference of minimum BD estimators and classifiers for varying-dimensional models
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
آنالیز عددی
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
Stochastic modeling for large-scale datasets usually involves a varying-dimensional model space. This paper investigates the asymptotic properties, when the number of parameters grows with the available sample size, of the minimum-BD estimators and classifiers under a broad and important class of Bregman divergence (BD), which encompasses nearly all of the commonly used loss functions in the regression analysis, classification procedures and machine learning literature. Unlike the maximum likelihood estimators which require the joint likelihood of observations, the minimum-BD estimators are useful for a range of models where the joint likelihood is unavailable or incomplete. Statistical inference tools developed for the class of large dimensional minimum-BD estimators and related classifiers are evaluated via simulation studies, and are illustrated by analysis of a real dataset.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Multivariate Analysis - Volume 101, Issue 7, August 2010, Pages 1574-1593
Journal: Journal of Multivariate Analysis - Volume 101, Issue 7, August 2010, Pages 1574-1593
نویسندگان
Chunming Zhang,