基于正态尺度混合因子模型的稳健贝叶斯分析及其应用被引量：1

Robust Bayesian Analysis and its Applications for Factor Analytic Model with Normal Scale Mixing

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摘要为了消除分布的偏移和异常点对统计推断的影响,本文基于正态尺度混合,对一般的因子分析模型展开稳健贝叶斯分析.Gibbs抽样器被用来从后验分布产生随机样本,统计推断基于后验经验分布展开.实际数据表明方法是有效的. To down-weight the influence of the distributional deviations and outliers, in this paper, we carry out robust Bayesian analysis for general factor analytic model combined with normal scale mixture model. Gibbs sampler is used to draw random observations from the posterior. Statistical inferences are carried out based on the empirical distribution of these observations. Two real data sets are analyzed to illustrate the effectiveness of the proposed method.

作者夏业茂刘应安

机构地区南京林业大学理学院南京林业大学信息技术学院

出处《应用概率统计》 CSCD 北大核心 2014年第4期423-438,共16页 Chinese Journal of Applied Probability and Statistics

基金南京林业大学高学历人才项目(163101004) 南京市科技择优资助项目(013101001)资助

关键词因子分析模型 BAYES估计 MCMC方法尺度混合模型 Factor analytic model, Bayesian estimation, MCMC algorithm, normal scale mixture model.

分类号 O212.8 [理学—概率论与数理统计] O212.4 [理学—概率论与数理统计]

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1Tanner, M.A. and Wong, W.H., The calculation of posterior distributions by data augmentation (with discussion), Journal of the American Statistical Association, 82(398)(1987), 528-550.
2Mardia, K.V., Kent, J.T. and Bibby, J.M., Multivariate Analysis, New York: Academic Press Inc., 1979.
3Joreskog, K.G. and SSrbom, D., LISREL 8: Structural Equation Modeling with the SIMPLIS Com- mand Language, Scientific Software International: Hove and London, 1996.
4Celeux, G., Forbes, F., Robert, C.P. and Titterington, D.M., Deviance information criteria for missing data models, Bayesian Analysis, 1(4)(2006), 651-673.
5Ritter, C. and Tanner, M.A., Facilitating the Gibbs sampler: the Gibbs stopper and the Griddy-Gibbs sampler, Journal of the American Statistical Association, 87(419)(1992), 861-868.
6Henisz, W.J., Politic and International Investment; Measuring Risk and Protecting Profits, London: Edward Elgar, 2002b.
7Muthen, B., LISCOMP: Analysis of Linear Structural Equations with a Comprehensive Measurement Model, Chicago: Scientific Software Inc., 1987.
8Skrondal, A. and Rabe-Hesketh, S., Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models, Chapman and Hall/CRC, 2004.
9Lee, S.Y., Structural Equation Modeling: A Bayesian Approach, New York: Springer-Verlag, 2007.
10Browne, M.W. and Shapiro, A., Robustness of normal theory methods in the analysis of linear latent variate models, British Journal of Mathematical and Statistical Psychology, 41(2)(1988), 193-208.

二级参考文献20

1Bollen, K.A., Structural Equations with Latent Variable, New York: Wiley, 1989.
2Bentler, P.M. and Dudgeon, P., Covariance structure analysis: statistical practice, theory, and direc- tions, Annual Review of Psychology, 47(1996), 541-570.
3Vonesh, E.F., Wang, H. and Majumdar, D., Generalized least squares, Taylor series linearization, and Fisher's scoring in multivariate nonlinear regression, Journal of the American Statistics Association, 96(2001), 282-291.
4Browne, M.W, Asymptotically distribution-free methods for the analysis of covariance structures, British Journal of mathematical and statistical Psychology, 37(1984), 62- 83.
5Huber, P.J.: Robust Statistics, New York: Wiley, 1981.
6Maronna, R.A., Robust M-estimate of multivariate location and scatter, Annals of Statistics, 4(1976), 51-67.
7Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J. and Stahel, W.A., Robust Statistics: The Approach Based on Influence Functions, New York: Wiley, 1986.
8Lee, S.Y. and Xia, Y.M., Maximum likelihood methods in treating outliers.and symmetrically heavy-tailed distributions for nonlinear structural equation models with missing data, Psyehome- trika, 71(2006), 565-585.
9Xia, Y.M., Song, X.Y. and Lee, S.Y., Robust model fitting for the nonlinear structural equa- tion model under normal theory, British Journal of Mathematical and Statistical Psychology, DOI: 10.1348/000711008X345966, 2008.
10Muthen, B. and Kaplan, D., A comparison of some methodolodies for the factor analysis of non-normal Likert variables, British Journal of Mathematical and Statistical Psychology, 38(1992), 171-189.