کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
13430358 | 1842416 | 2020 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Feature screening for ultrahigh dimensional categorical data with covariates missing at random
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نظریه محاسباتی و ریاضیات
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چکیده انگلیسی
Most existing feature screening methods assume that data are fully observed. It is quite a challenge to develop screening methods for incomplete data since the traditional missing data analysis techniques cannot be directly applied to ultrahigh dimensional case. A two-step model-free feature screening procedure for ultrahigh dimensional categorical data when some covariate values are missing at random is developed. For each covariate with missing data, the first step screens out the variables in the unspecified propensity function. In the second step, screening statistics such as the adjusted Pearson Chi-Square statistics can be calculated by leveraging the variables obtained in the first step and the special structure of categorical data. Sure screening properties are established for the proposed method. Finite sample performance is investigated by simulation studies and a real data example.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 142, February 2020, 106824
Journal: Computational Statistics & Data Analysis - Volume 142, February 2020, 106824
نویسندگان
Lyu Ni, Fang Fang, Jun Shao,