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Activity Number: 61 - Approaches for Modeling Clustered and Longitudinal Data
Type: Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
Sponsor: Biometrics Section
Abstract #312253
Title: Conway-Maxwell-Multinomial Regression for Categorical Data with Associated Trials
Author(s): Darcy Morris* and Andrew M. Raim and Kimberly Sellers
Companies: and U.S. Census Bureau and Georgetown University
Keywords: Multinomial Regression; COM-Poisson; Count Data; Clustered Data; Categorical Data

Categorical data are often observed as counts resulting from a fixed number of trials in which each trial consists of making one selection from a prespecified set of categories. Multinomial regression serves as a standard model for such clustered data but assumes that trials are independent and identically distributed given the covariates. This work considers Conway-Maxwell-multinomial (CMM) regression for modeling clustered categorical data exhibiting positively or negatively associated trials. The CMM distribution features a dispersion parameter which allows it to adapt to a range of association levels that may depend on observed explanatory variables. Using public data from the U.S. Census Bureau, we describe a CMM regression model of a household categorical variable that may exhibit geographic association. The results illustrate insight gained from estimating characteristics of trial-level association.

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

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