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Activity Number: 320 - Practical and Realistic Variable Selection Methods
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract #326907
Title: Generalized CP and the Bootstrap for Variable Selection in Moderate or High-Dimensional Data
Author(s): Lawrence D Brown and Junhui Cai* and Linda Zhao
Companies: University of Pennsylvania and University of Pennsylvania and University of Pennsylvania
Keywords: Model Selection; Generalized CP; Bootstrap; Model Lean

Linear models as working models have performed very well in practice. But most often the theoretical properties are obtained under the usual linear model assumptions such as linearity, homoscedasticity and normality. Using the least squared estimators we justify their desirable properties under much broader model assumptions, namely a model lean framework. Generalized CP (GCP) is proposed to estimate the prediction errors. We study its properties. An alternative bootstrap method is also investigated. Model selections are done through both methods.

Joint work with Junhui Cai, Linda Zhao and the Wharton group

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

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