Maximum likelihood methods in treating outliers and symmetrically heavy-tailed distributions for nonlinear structural equation models with missing data
Publication in refereed journal

香港中文大學研究人員

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摘要By means of more than a dozen user friendly packages, structural equation models (SEMs) are widely used in behavioral, education, social, and psychological research. As the underlying theory and methods in these packages are vulnerable to outliers and distributions with longer-than-normal tails, a fundamental problem in the field is the development of robust methods to reduce the influence of outliers and the distributional deviation in the analysis. In this paper we develop a maximum likelihood (ML) approach that is robust to outliers and symmetrically heavy-tailed distributions for analyzing nonlinear SEMs with ignorable missing data. The analytic strategy is to incorporate a general class of distributions into the latent variables and the error measurements in the measurement and structural equations. A Monte Carlo EM (MCEM) algorithm is constructed to obtain the ML estimates, and a path sampling procedure is implemented to compute the observed-data log-likelihood and then the Bayesian information criterion for model comparison. The proposed methodologies are illustrated with simulation studies and an example.
著者Lee SY, Xia YM
期刊名稱Psychometrika
出版年份2006
月份9
日期1
卷號71
期次3
出版社SPRINGER
頁次565 - 585
國際標準期刊號0033-3123
電子國際標準期刊號1860-0980
語言英式英語
關鍵詞Bayesian information criterion; Gibbs sampler; heavy-tailed distributions; MCEM algorithm; outliers; path sampling
Web of Science 學科類別Mathematical Methods In Social Sciences; Mathematics; Mathematics, Interdisciplinary Applications; MATHEMATICS, INTERDISCIPLINARY APPLICATIONS; Psychology; Psychology, Mathematical; PSYCHOLOGY, MATHEMATICAL; Social Sciences, Mathematical Methods; SOCIAL SCIENCES, MATHEMATICAL METHODS

上次更新時間 2022-10-01 於 23:55