Text

Covariate Information Number for Feature Screening in Ultrahigh-Dimensional Supervised Problems

Contemporary high-throughput experimental and surveying techniques give rise to ultrahigh-dimensional supervised problems with sparse signals; that is, a limited number of observations (n), each with a very large number of covariates (p≫n) , only a small share of which is truly associated with the response. In these settings, major concerns on computational burden, algorithmic stability, and statistical accuracy call for substantially reducing the feature space by eliminating redundant covariates before the use of any sophisticated statistical analysis. Along the lines of Pearson’s correlation coefficient-based sure independence screening and other model- and correlation-based feature screening methods, we propose a model-free procedure called covariate information number-sure independence screening (CIS). CIS uses a marginal utility connected to the notion of the traditional Fisher information, possesses the sure screening property, and is applicable to any type of response (features) with continuous features (response). Simulations and an application to transcriptomic data on rats reveal the comparative strengths of CIS over some popular feature screening methods. Supplementary materials for this article are available online.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (JASA); Vol.117 No.539 September 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1516-1529

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

SURE INDEPENDENCE SCREENING
FISHER INFORMATION
ULTRAHIGH DIMENSION
AFFYMETRIX GENECHIP RAT GENOME 230 2.0 ARRAY
MODEL-FREE
SUPERVISED PROBLEMS

Info Detail Spesifik

https://doi.org/10.1080/01621459.2020.1864380

Pernyataan Tanggungjawab

Debmalya Nandy, Francesca Chiaromonte, Runze Li

Versi lain/terkait

Lampiran Berkas

Komentar