A note on the binomial model with simplex constraints

Guo-Liang Tian, Kai Wang Ng and Philip L.H. Yu ()

Computational Statistics & Data Analysis, 2011, vol. 55, issue 12, pages 3381-3385

Abstract: Liu (2000) considered maximum likelihood estimation and Bayesian estimation in a binomial model with simplex constraints using the expectation-maximization (EM) and data augmentation (DA) algorithms. By introducing latent variables {Zij} and {Yij} (to be defined later), he formulated the constrained parameter problem into a missing data problem. However, the derived DA algorithm does not work because he actually assumed that the {Yij} are known. Furthermore, although the final results from the derived EM algorithm are correct, his findings are based on the assumption that the {Yij} are observable. This note provides a correct DA algorithm. In addition, we obtained the same E-step and M-step under the assumption that the {Yij} are unobservable. A real example is used for illustration.

Keywords: Constrained; binomial; model; DA; algorithm; EM; algorithm; Simplex; constraints (search for similar items in EconPapers)
Date: 2011
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Persistent link: http://EconPapers.repec.org/RePEc:eee:csdana:v:55:y:2011:i:12:p:3381-3385

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