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Activity Number:
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170
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Type:
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Contributed
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Date/Time:
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Monday, August 3, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #303264 |
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Title:
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Analysis of Multivariate Skew Normal Models with Incomplete Data
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Author(s):
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Tsung-I Lin*+ and Hsiu-Jung Ho and Chiang-Ling Chen
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Companies:
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National Chung Hsing University and National Chung Hsing University and National Chung Hsing University
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Address:
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Department of Applied Mathematics, Taichung, 402, Taiwan
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Keywords:
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ECM algorithm ; Gibbs sampler ; MSN model ; multiple imputation ; multivariate truncated normal ; posterior distributions
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Abstract:
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We establish computationally flexible methods and algorithms for the analysis of multivariate skew normal models when missing values occur in the data. To facilitate the computation and simplify the theoretic derivation, two auxiliary indicator matrices are incorporated into the model for the determination of observed and missing components of each observation. Under missing at random mechanisms, we formulate an analytically simple ECM algorithm for calculating parameter estimation and retrieving each missing value with a single-valued imputation. Gibbs sampling is used to perform a Bayesian inference on model parameters and to create multiple imputations for missing values. The proposed methodologies are illustrated through a real data set and comparisons are made with those obtained from fitting the normal counterparts.
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