A1287
Title: On sufficient variable screening using log odds ratio filter
Authors: Wenbo Wu - University of Texas at San Antonio (United States) [presenting]
Abstract: For ultrahigh-dimensional data, variable screening is an important step to reduce the scale of the problem, hence, improving the estimation accuracy and efficiency. A new dependence measure which is called the log odds ratio statistic to be used under the sufficient variable screening framework. The sufficient variable screening approach ensures the sufficiency of the selected input features in modelling the regression function and is an enhancement of existing marginal screening methods. In addition, an ensemble variable is the proposed screening approach to combine the proposed fused log odds ratio filter with the fused Kolmogorov filter to achieve supreme performance by taking advantage of both filters. The sure screening properties of the fused log odds ratio filter for both marginal variable screening and sufficient variable screening are established. Extensive simulations and a real data analysis are provided to demonstrate the usefulness of the proposed log odds ratio filter and the sufficient variable screening procedure.
