What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Regression analysis
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 most cited work include:
- Structural Equation Modeling: A Bayesian Approach (348 citations)
- Evaluation of the Bayesian and Maximum Likelihood Approaches in Analyzing Structural Equation Models with Small Sample Sizes. (277 citations)
- Local Polynomial Fitting in Semivarying Coefficient Model (218 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Structural equation modeling (46.81%)
- Statistics (38.83%)
- Applied mathematics (40.43%)
What were the highlights of his more recent work (between 2006-2014)?
- Structural equation modeling (46.81%)
- Econometrics (25.00%)
- Applied mathematics (40.43%)
In recent papers he was focusing on the following fields of 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.
Between 2006 and 2014, his most popular works were:
- Structural Equation Modeling: A Bayesian Approach (348 citations)
- Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences (97 citations)
- Perturbation selection and influence measures in local influence analysis (80 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Algebra
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.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
