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Activity Number: 656
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #320771 View Presentation
Title: A Nonparametric Test of Independence Between Two Variables
Author(s): Bin Li* and Qingzhao Yu
Companies: Louisiana State University and Louisiana State University Health Sciences Center
Keywords: concomitant ; variable screening ; variable selection ; nonparametric

A nonparametric statistic, called the roughness of concomitant ranks, is proposed for testing whether two quantitative vectors are dependent. Empirical evidence shows the new statistic is normally distributed with mean and variance are given in Theorem 1. The new testing procedure is highly computationally efficient and simple, and exhibits a competent empirical performance in simulations and two microarray data analysis. We apply the new method to deal with variable screening for high-dimensional data analysis. For low signal-to-noise ratio setting, we suggest to use data binning to increase the power of the test. Simulation results show the fine performance of the proposed method with existing screening methods.

Authors who are presenting talks have a * after their name.

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