Inferences for Mixtures of Distributions for Centrally Censored Data with Partial Identification
Por:
Campos, D, Martinez, CE, Contreras-Cristan, A, O'Reilly, F
Publicada:
1 ene 2010
Categoría:
Statistics and Probability
Resumen:
In this article, several methods to make inferences about the parameters of a finite mixture of distributions in the context of centrally censored data with partial identification are revised. These methods are an adaptation of the work in Contreras-Cristan, Gutierrez-Pena, and O'Reilly (2003) in the case of right censoring. The first method focuses on an asymptotic approximation to a suitably simplified likelihood using some latent quantities; the second method is based on the expectation-maximization (EM) algorithm. Both methods make explicit use of latent variables and provide computationally efficient procedures compared to non-Bayesian methods that deal directly with the full likelihood of the mixture appealing to its asymptotic approximation. The third method, from a Bayesian perspective, uses data augmentation to work with an uncensored sample. This last method is related to a recently proposed Bayesian method in Baker, Mengersen, and Davis (2005). Our proposal of the three adap
Filiaciones:
Campos, D:
Univ Nacl Autonoma Mexico, Mexico City 01000, DF, Mexico
Contreras-Cristan, A:
Univ Nacl Autonoma Mexico, Mexico City 01000, DF, Mexico
O'Reilly, F:
Univ Nacl Autonoma Mexico, Mexico City 01000, DF, Mexico
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