A Conway–Maxwell-multinomial distribution for flexible modeling of clustered categorical data

Darcy Steeg Morris, Andrew M. Raim and Kimberly F. Sellers

Journal of Multivariate Analysis, 2020, vol. 179, issue C

Abstract: 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. The multinomial distribution serves as a standard model for such data but assumes that trials are independent and identically distributed. Extensions such as the Dirichlet-multinomial and random-clumped multinomial distribution can express positive association, where trials are more likely to result in a common category due to membership in a common cluster. This work considers a Conway–Maxwell-multinomial (CMM) distribution 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 and includes several recognizable distributions as special cases. We explore properties of CMM, illustrate its flexible characteristics, identify a method to efficiently compute maximum likelihood (ML) estimates, present simulations of small sample properties under ML estimation, and demonstrate the model via data analysis examples.

Keywords: Multivariate discrete distributions; Conway–Maxwell–Poisson distribution; Exponential family; Dispersion; Multinomial regression (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.jmva.2020.104651

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