Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/37057
標題: Analysis of multivariate skew normal models with incomplete data
作者: Lin, T.I.
林宗儀
Ho, H.J.
Chen, C.L.
關鍵字: ECM algorithm
Gibbs sampler
MSN model
Multiple imputation
Multivariate truncated normal
Posterior distributions
maximum-likelihood-estimation
t-distribution
posterior distributions
bayesian-analysis
unknown number
mixture-models
em algorithm
missing
data
inference
components
期刊/報告no:: Journal of Multivariate Analysis, Volume 100, Issue 10, Page(s) 2337-2351.
摘要: 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. (C) 2009 Elsevier Inc. All rights reserved.
URI: http://hdl.handle.net/11455/37057
ISSN: 0047-259X
文章連結: http://dx.doi.org/10.1016/j.jmva.2009.07.005
Appears in Collections:統計學研究所

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