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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Hypothesis testing sure independence screening for nonparametric regression

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Zambom, Adriano Zanin ; Akritas, Michael G.
Total Authors: 2
Document type: Journal article
Source: ELECTRONIC JOURNAL OF STATISTICS; v. 12, n. 1, p. 767-792, 2018.
Web of Science Citations: 0

In this paper we develop a sure independence screening method based on hypothesis testing (HT-SIS) in a general nonparametric regression model. The ranking utility is based on a powerful test statistic for the hypothesis of predictive significance of each available covariate. The sure screening property of HT-SIS is established, demonstrating that all active predictors will be retained with high probability as the sample size increases. The threshold parameter is chosen in a theoretically justified manner based on the desired false positive selection rate. Simulation results suggest that the proposed method performs competitively against procedures found in the literature of screening for several models, and outperforms them in some scenarios. A real dataset of microarray gene expressions is analyzed. (AU)

FAPESP's process: 12/10808-2 - Algorithm for Hypothesis Testing in Nonparametric Regression and its Asymptotic Properties with Applications to Variable Selection
Grantee:Adriano Zanin Zambom
Support type: Scholarships in Brazil - Post-Doctorate