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

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

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 99 (2008)
    Issue (Month): 3 (March)
    Pages: 490-509

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    Handle: RePEc:eee:jmvana:v:99:y:2008:i:3:p:490-509

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    Related research

    Keywords: Beta-Liouville distribution Data augmentation Dirichlet distribution EM algorithm Grouped Dirichlet distribution Incomplete categorical data;

    References

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    1. Geng, Zhi & Li, Kaican, 2003. "Factorization of posteriors and partial imputation algorithm for graphical models with missing data," Statistics & Probability Letters, Elsevier, vol. 64(4), pages 369-379, October.
    2. Gupta, Rameshwar D. & Richards, Donald St. P., 1992. "Multivariate Liouville distributions, III," Journal of Multivariate Analysis, Elsevier, vol. 43(1), pages 29-57, October.
    3. Zhi Geng, 2000. "Mixed Graphical Models with Missing Data and the Partial Imputation EM Algorithm," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics & Finnish Statistical Society & Norwegian Statistical Association & Swedish Statistical Association, vol. 27(3), pages 433-444.
    4. Gupta, Rameshwar D. & Richards, Donald St.P., 1987. "Multivariate Liouville distributions," Journal of Multivariate Analysis, Elsevier, vol. 23(2), pages 233-256, December.
    5. Liu, Chuanhai, 1999. "Efficient ML Estimation of the Multivariate Normal Distribution from Incomplete Data," Journal of Multivariate Analysis, Elsevier, vol. 69(2), pages 206-217, May.
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    Cited by:
    1. Tian, Guo-Liang & Tang, Man-Lai & Yuen, Kam Chuen & Ng, Kai Wang, 2010. "Further properties and new applications of the nested Dirichlet distribution," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 394-405, February.

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