Text

Model-Free Variable Selection With Matrix-Valued Predictors

We introduce a novel framework for model-free variable selection with matrix-valued predictors. To test the importance of rows, columns, and submatrices of the predictor matrix in terms of predicting the response, three types of hypotheses are formulated under a unified framework. The asymptotic properties of the test statistics under the null hypothesis are established and a permutation testing algorithm is also introduced to approximate the distribution of the test statistics. A maximum ratio criterion (MRC) is proposed to facilitate the model-free variable selection. Unlike the traditional stepwise regression procedures that require calculating p-values at each step, the MRC is a noniterative procedure that does not require p-value calculation and is guaranteed to achieve variable selection consistency under mild conditions. Performance of the proposed method is evaluated in extensive simulations and demonstrated through the analysis of an electroencephalography data. Supplementary materials for this article are available online.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS; Vol.30 No.1 March 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 171 - 181

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

PERMUTATION TEST
MATRIX NORMAL DISTRIBUTION
MATRIX-VALUED PREDICTORS
MAXIMUM RATIO CRITERION
MODEL-FREE VARIABLE SELECTION

Info Detail Spesifik

https://doi.org/10.1080/10618600.2020.1806854

Pernyataan Tanggungjawab

Zeda Li, Yuexiao Dong

Versi lain/terkait

Lampiran Berkas

Komentar