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Activity Number: 544
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #318905
Title: Selection for Semiparametric Odds Ratio Model via Adaptive Screening
Author(s): Jinsong Chen* and Huayun Chen
Companies: University of Illinois at Chicago and University of Illinois at Chicago
Keywords: Regression ; Partial faithfulness ; Neighborhood Selection

In network detection, the semiparametric odds ratio model (ORM) has several advantages: flexible in handling both discrete and continuous data and including interactions; avoiding the problem of model incompatibility; and invariant to biased sampling design. However, for high-dimensional data, the complexity of this model induces computational burden comparing with parametric approaches, e.g., Gaussian graphical model. In this paper, we adapt three novel methods for ORM: using ORM after marginal correlation screening; stepwise selection using partial odds ratio coefficient based on PC-simple algorithm; and using ORM with Lasso after marginal correlation screening. The theoretical supports of these methods are developed, and simulations are conducted to assess the performance of our methods.

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

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