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

Zhang, Jian and Oftadeh, Elaheh (2016) Principal Variable Analysis: Multivariate Variable Selection through Use of Null-Beamforming. TBD, . (Submitted) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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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 > Statistics
Depositing User: Jian Zhang
Date Deposited: 05 Sep 2016 16:37 UTC
Last Modified: 26 Sep 2016 10:30 UTC
Resource URI: (The current URI for this page, for reference purposes)
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