TY - JOUR
T1 - Covariate Information Number for Feature Screening in Ultrahigh-Dimensional Supervised Problems
AU - Nandy, Debmalya
AU - Chiaromonte, Francesca
AU - Li, Runze
N1 - Funding Information:
F. Chiaromonte and D. Nandy were supported by NSF grant DMS-1407639. R. Li was supported by NSF grants DMS-1820702, DMS-1953196, and DMS-2015539, and NIH grants R01CA229542, R01ES019672, and R21CA226300. We thank Drs. Bharath Sriperumbudur, Amal Agarwal, and Mauricio Nascimento for helping with theoretical derivations; Dr. Weixin Yao for MATLAB codes to compute Covariate Information Matrices; Dr. Paolo Inglese for MATLAB code to compute distance correlations; and Drs. Xiaofeng Shao and Jingsi Zhang for R code to compute martingale difference correlations, the transcriptomic data, and R codes for its preprocessing. We also thank members of the Makova Lab at Penn State and Binglan (Victoria) Li for helping with the transcriptomic data application. Finally, we are grateful to the anonymous reviewers and the associate editor for crucial feedback that helped us greatly to improve our work.
Publisher Copyright:
© 2021 American Statistical Association.
PY - 2022
Y1 - 2022
N2 - 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 (Formula presented.), 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.
AB - 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 (Formula presented.), 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.
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U2 - 10.1080/01621459.2020.1864380
DO - 10.1080/01621459.2020.1864380
M3 - Article
C2 - 36172297
AN - SCOPUS:85101058900
SN - 0162-1459
VL - 117
SP - 1516
EP - 1529
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 539
ER -