2004 - Fellow of the American Statistical Association (ASA)
His primary areas of study are Statistics, Structural equation modeling, Applied mathematics, Latent variable and Bayesian probability. His study in Structural equation modeling is interdisciplinary in nature, drawing from both Goodness of fit, Estimation theory and Covariance. Sik-Yum Lee interconnects Calculus, Mathematical optimization and Generalized least squares in the investigation of issues within Applied mathematics.
In his study, Quadratic equation, LISREL and Basis is strongly linked to Standard error, which falls under the umbrella field of Latent variable. In his study, which falls under the umbrella issue of Bayesian probability, Categorical variable is strongly linked to Econometrics. His Gibbs sampling research incorporates elements of Latent class model and Conditional probability distribution.
His primary scientific interests are in Structural equation modeling, Statistics, Applied mathematics, Econometrics and Latent variable. His Structural equation modeling research includes themes of Missing data, Asymptotic distribution, Categorical variable, Mathematical optimization and Bayesian probability. His Statistics study frequently intersects with other fields, such as Polychoric correlation.
His Applied mathematics research is multidisciplinary, relying on both Covariance, Covariance matrix, Estimator, Generalized least squares and Calculus. His Econometrics research is multidisciplinary, incorporating perspectives in Confirmatory factor analysis and Dirichlet process. Sik-Yum Lee has included themes like Latent class model, LISREL and Gibbs sampling in his Latent variable study.
His scientific interests lie mostly in Structural equation modeling, Econometrics, Applied mathematics, Bayesian probability and Latent variable. Sik-Yum Lee has researched Structural equation modeling in several fields, including Nonparametric statistics, Mathematical optimization, Categorical variable and Missing data. Sik-Yum Lee combines subjects such as Latent variable model, Dirichlet process and Confirmatory factor analysis with his study of Econometrics.
His research investigates the connection between Applied mathematics and topics such as Calculus that intersect with problems in Linear model, Fisher information and Expectation–maximization algorithm. His research in Bayesian probability intersects with topics in Exponential family and Computational statistics. His work is dedicated to discovering how Gibbs sampling, Bayes factor are connected with Conjugate prior and other disciplines.
Sik-Yum Lee mainly investigates Structural equation modeling, Latent variable, Econometrics, Statistics and Latent variable model. His Structural equation modeling study which covers Bayesian probability that intersects with Machine learning. Sik-Yum Lee usually deals with Latent variable and limits it to topics linked to Variable-order Bayesian network and Contrast, Structure and Data science.
Statistics is represented through his Missing data, Bayesian linear regression and Covariance research. He has researched Missing data in several fields, including Multivariate statistics and Applied mathematics. While the research belongs to areas of Latent variable model, Sik-Yum Lee spends his time largely on the problem of Gibbs sampling, intersecting his research to questions surrounding Bayesian hierarchical modeling, Conjugate prior and Prior probability.
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Structural Equation Modeling: A Bayesian Approach
Evaluation of the Bayesian and Maximum Likelihood Approaches in Analyzing Structural Equation Models with Small Sample Sizes.
Sik-Yum Lee;Xin-Yuan Song.
Multivariate Behavioral Research (2004)
Local Polynomial Fitting in Semivarying Coefficient Model
Wenyang Zhang;Sik-Yum Lee;Xinyuan Song.
Journal of Multivariate Analysis (2002)
A two-stage estimation of structural equation models with continuous and polytomous variables.
Sik-Yum Lee;Wai-Yin Poon;P. M. Bentler.
British Journal of Mathematical and Statistical Psychology (1995)
Local influence for incomplete data models
Hong Tu Zhu;Sik Yum Lee.
Journal of The Royal Statistical Society Series B-statistical Methodology (2001)
A study of algorithms for covariance structure analysis with specific comparisons using factor analysis
Sik-yum Lee;R. I. Jennrich.
Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences
Xin-Yuan Song;Sik-Yum Lee.
Maximum likelihood estimation of multivariate polyserial and polychoric correlation coefficients
Wai-Yin Poon;Sik-Yum Lee.
STRUCTURAL EQUATION MODELS WITH CONTINUOUS AND POLYTOMOUS VARIABLES
Sik-Yum Lee;Wai-Yin Poon;P. M. Bentler.
Latent variable models with mixed continuous and polytomous data
J.-Q. Shi;S.-Y. Lee.
Journal of The Royal Statistical Society Series B-statistical Methodology (2000)
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