• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.71-79
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• 2013

Semiparametric and nonparametric small area estimations have been studied to overcome a large variance due to a small sample size allocated in a small area. In this study, we investigate semiparametric and nonparametric mixed effect small area estimators using penalized spline and kernel smoothing methods respectively and compare their performances using labor statistics.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.385-392
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• 2012

Logistic regression is a well known binary classification method in the field of statistical learning. Mixed-effect regression models are widely used for the analysis of correlated data such as those found in longitudinal studies. We consider kernel extensions with semiparametric fixed effects and parametric random effects for the logistic regression. The estimation is performed through the penalized likelihood method based on kernel trick, and our focus is on the efficient computation and the effective hyperparameter selection. For the selection of optimal hyperparameters, cross-validation techniques are employed. Numerical results are then presented to indicate the performance of the proposed procedure.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.923-932
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• 2008

We are likely to face complex multivariate data which can be characterized by having a non-trivial correlation structure. For instance, omitted covariates may simultaneously affect more than one count in clustered data; hence, the modeling of the correlation structure is important for the efficiency of the estimator and the computation of correct standard errors, i.e., valid inference. A standard way to insert dependence among counts is to assume that they share some common unobservable variables. For this assumption, we fitted correlated random effect models considering multilevel model. Estimation was carried out by adopting the semiparametric approach through a finite mixture EM algorithm without parametric assumptions upon the random coefficients distribution.

• Journal of Labour Economics
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• v.29 no.2
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• pp.67-116
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• 2006

This study directly analyzes the wage distributions rather than indirectly looking at a few of their moments. It also investigates wage distributions using various descriptive and semi-parametric methods. The wage distributions of Korean manufacturing industries can in general be represented by three distinct forms, underdeveloped, advanced and the medium of the two. The discrepancies in these distribution forms are explained by differences in the labor-type distributions and their weights in the composition of wage distribution forms, and further clarified through various descriptive statistics based on them. However, the descriptive statistical analysis has a limit in that it shows mixed outcomes of different categoric variables. Then, this problem is resolved by applying a semi-parametric estimation of hazard function and the marginal effect evaluations of variable changes on estimated distributions not on the function. As a result of this marginal analysis, the common features and differences of categoric variables and their intensities of effects on distributions are revealed.