Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 215 - Contributed Poster Presentations: Section on Statistical Learning and Data Science
Type: Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #313820
Title: Stagewise Estimating Equations for Variable Selection with Longitudinal Rate Data
Author(s): Gregory Vaughan*
Companies: Bentley University
Keywords: GEE; clustered data; model selection; penalized regression; sparsity; stagewise estimation

In many fields, there is a need for modeling rate data where response measures are reported as a percentage or proportion and are thus bounded from 0 to 1; i.e. in Psychology, many metrics fall on a bounded scale, which can be scaled to fall between $0$ and $1$, or one may want to model the proportion of subscribers successfully signed up for a service such as cable. Though beta regression has been proposed to model rate data, beta regression is not designed to work with longitudinal or clustered data where the individual observations may have some correlation within clusters and little work has been done to explore model selection in the context of modeling rate data. This work presents a model selection technique utilizing a stagewise approach that easily allows for longitudinal rate data. Numerical studies are presented to demonstrate the efficacy of the proposed approach.

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

Back to the full JSM 2020 program