Statistical analysis with missing data
Statistical analysis with missing data
Efficient ML estimation of the multivariate normal distribution from incomplete data
Journal of Multivariate Analysis
A multivariate skew normal distribution
Journal of Multivariate Analysis
On fundamental skew distributions
Journal of Multivariate Analysis
Maximum likelihood estimation for multivariate skew normal mixture models
Journal of Multivariate Analysis
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian inference for the multivariate skew-normal model: A population Monte Carlo approach
Computational Statistics & Data Analysis
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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 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 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.