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Analysis of multivariate skew normal models with incomplete data. (English) Zbl 1175.62054

Summary: 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 computations and simplify the theoretic derivations, two auxiliary permutation 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 expectation conditional maximization (ECM) algorithm for calculating parameter estimation and retrieving each missing value with a single-valued imputation. Gibbs sampling is used to perform 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.

MSC:

62H12 Estimation in multivariate analysis
62F15 Bayesian inference
62H10 Multivariate distribution of statistics
65C60 Computational problems in statistics (MSC2010)

Software:

BayesDA
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References:

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