Grouped Dirichlet distribution: A new tool for incomplete categorical data analysis

  • Authors:

  • Kai Wang Ng;Man-Lai Tang;Ming Tan;Guo-Liang Tian

  • Affiliations:

  • Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong, PR China;Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong, PR China;Division of Biostatistics, University of Maryland Greenebaum Cancer Center, 22 South Greene Street, Baltimore, MD 21201, USA;Division of Biostatistics, University of Maryland Greenebaum Cancer Center, 22 South Greene Street, Baltimore, MD 21201, USA

  • Venue:
  • Journal of Multivariate Analysis
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

Motivated by the likelihood functions of several incomplete categorical data, this article introduces a new family of distributions, grouped Dirichlet distributions (GDD), which includes the classical Dirichlet distribution (DD) as a special case. First, we develop distribution theory for the GDD in its own right. Second, we use this expanded family as a new tool for statistical analysis of incomplete categorical data. Starting with a GDD with two partitions, we derive its stochastic representation that provides a simple procedure for simulation. Other properties such as mixed moments, mode, marginal and conditional distributions are also derived. The general GDD with more than two partitions is considered in a parallel manner. Three data sets from a case-control study, a leprosy survey, and a neurological study are used to illustrate how the GDD can be used as a new tool for analyzing incomplete categorical data. Our approach based on GDD has at least two advantages over the commonly used approach based on the DD in both frequentist and conjugate Bayesian inference: (a) in some cases, both the maximum likelihood and Bayes estimates have closed-form expressions in the new approach, but not so when they are based on the commonly-used approach; and (b) even if a closed-form solution is not available, the EM and data augmentation algorithms in the new approach converge much faster than in the commonly-used approach.