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National Chung Hsing University Institutional Repository - NCHUIR > 理學院 > 統計學研究所 > 依資料類型分類 > 期刊論文 >  Analysis of multivariate skew normal models with incomplete data

Please use this identifier to cite or link to this item: http://nchuir.lib.nchu.edu.tw/handle/309270000/133588

標題: Analysis of multivariate skew normal models with incomplete data
作者: Lin, T.I.;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
日期: 2009
Issue Date: 2012-12-14 10:05:47 (UTC+8)
關連: 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.
Relation: Journal of Multivariate Analysis
Appears in Collections:[依教師分類] 陳正倫
[依資料類型分類] 期刊論文
[依教師分類] 林宗儀
[依教師分類] 林宗儀

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