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Multivariate Variable Selection through Use of Null-Beamforming: Principle Variable Analysis

Zhang, Jian, Oftadeh, Elaheh (2016) Multivariate Variable Selection through Use of Null-Beamforming: Principle Variable Analysis. TBD, . (Submitted)

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Abstract

This article extends the idea of principal component analysis to multivariate variable selection. The basic premise behind the proposal is to scan through a predictor variable space with a series of filters called null-beamformers; each is tailored to a particular region in the space and resistant to interference effects originating from other regions. This gives rise to a predictive power map for predictor selection. The new approach attempts to explore the maximum amount of variation in the data with a small number of principal variables. Applying the proposal to simulated data and real cancer drug data, we show that it outperforms the existing methods in terms of sensitivity and specificity.

Item Type: Article
Uncontrolled keywords: High dimensional and multivariate regression models, principal variable analysis, variable selection and null-beamforming.
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jian Zhang
Date Deposited: 16 Oct 2017 14:30 UTC
Last Modified: 29 May 2019 19:42 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/64042 (The current URI for this page, for reference purposes)

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