کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4949214 1440045 2017 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Penalized principal logistic regression for sparse sufficient dimension reduction
ترجمه فارسی عنوان
رگرسیون لجستیک اصلی را برای کاهش ابعاد کوچک کافی محروم کرد
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
چکیده انگلیسی
Sufficient dimension reduction (SDR) is a successive tool for reducing the dimensionality of predictors by finding the central subspace, a minimal subspace of predictors that preserves all the regression information. When predictor dimension is large, it is often assumed that only a small number of predictors is informative. In this regard, sparse SDR is desired to achieve variable selection and dimension reduction simultaneously. We propose a principal logistic regression (PLR) as a new SDR tool and further develop its penalized version for sparse SDR. Asymptotic analysis shows that the penalized PLR enjoys the oracle property. Numerical investigation supports the advantageous performance of the proposed methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 111, July 2017, Pages 48-58
نویسندگان
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