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
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات آنالیز عددی
پیش نمایش صفحه اول مقاله
Statistical inference of minimum BD estimators and classifiers for varying-dimensional models
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
نویسندگان
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