• Journal of the Korean Statistical Society
• /
• v.33 no.4
• /
• pp.435-447
• /
• 2004

In additive nonparametric regression, Linton and Nielsen (1995) showed that the marginal integration when applied to the local linear smoother produces a rate-optimal estimator of each univariate component function for the case where the dimension of the predictor is two. In this paper we give new formulas for the bias and variance of the marginal integration regression estimators which are valid for boundary areas as well as fixed interior points, and show the local linear marginal integration estimator is in fact rate-optimal when the dimension of the predictor is less than or equal to four. We extend the results to the case of the local polynomial smoother, too.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.17 no.6
• /
• pp.54-59
• /
• 2003

This paper presents hourly system marginal price forecasting of the Korea electric power system using a fuzzy linear regression analysis method. The proposed method is tested by forecasting hourly system marginal price for a week of spring in 2002. The percent average of forecasting error for the proposed method is from 3.14% to 6.10% in the weekdays, from 7.04% to 8.22% in the weekends, and comparable with a artificial neural networks method.

• Communications for Statistical Applications and Methods
• /
• v.11 no.2
• /
• pp.381-397
• /
• 2004

The marginal likelihood has become an important tool for model selection in Bayesian analysis because it can be used to rank the models. We discuss the marginal likelihood for Poisson regression models that are potentially useful in small area estimation. Computation in these models is intensive and it requires an implementation of Markov chain Monte Carlo (MCMC) methods. Using importance sampling and multivariate density estimation, we demonstrate a computation of the marginal likelihood through an output analysis from an MCMC sampler.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.1
• /
• pp.141-150
• /
• 2015

Graphical paradigms for assessing the adequacy of models in logistic regression are discussed. The residual plot has been widely used as a graphical tool for evaluating the adequacy of the model. However, this approach works well only for linear models with constant variance, and the alternative approach, the marginal model plot, has its defects as well. We suggest a Chi-residual plot that overcomes the potential shortcomings of the marginal model plot.

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.1
• /
• pp.21-32
• /
• 2010

Graphical regression is a paradigm for obtaining regression information using plots without model assumptions. The general goal of this approach is to find lowdimensional sufficient summary plots without loss of important information. Model assessments using residual plots are less likely to be successful in models that are not linear. As an alternative approach, marginal model plots provide a general graphical method for assessing the model. We apply the methods of graphical regression and model assessment using marginal model plots to the logistic regression model.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.21 no.2
• /
• pp.67-77
• /
• 2016

This study developed synthetic regression diagram for analyzing the linear trend of sea surface height, temperature, and salinity around the Korean marginal seas. In situ observed data had been quality controlled and they were verified by EOF comparison with objective analyzed data. From the synthetic regression diagram, we confirmed similar linear regression values with those of previous studies, but additionally provided detailed regression rate of each 5 to 30 year for the total periods of 1983-2013. We expect that quantitative results presented by this study will be useful as standard reference numbers for relevant studies analyzing oceanic long-term trend.

• Journal of the Korean Data and Information Science Society
• /
• v.24 no.4
• /
• pp.877-883
• /
• 2013

The quadratic inference functions (QIF) method proposed by Qu et al. (2000) and the generalized method of moments (GMM) for marginal regression analysis of longitudinal data with time-dependent covariates proposed by Lai and Small (2007) both are the methods based on generalized method of moment (GMM) introduced by Hansen (1982) and both use generalized estimating equations (GEE). Lai and Small (2007) divided time-dependent covariates into three types such as: Type I, Type II and Type III. In this paper, we compared these methods in the case of Type II and Type III in which full covariates conditional mean assumption (FCCM) is violated and interested in whether they can improve the results of GEE with independence working correlation. We show that in the marginal regression model with Type II time-dependent covariates, GMM Type II of Lai and Small (2007) provides more ecient result than QIF and for the Type III time-dependent covariates, QIF with independence working correlation and GMM Type III methods provide the same results. Our simulation study showed the same results.

• Journal of the Korean Statistical Society
• /
• v.26 no.2
• /
• pp.261-276
• /
• 1997

In the context of binary response regression, the problem of constructing Bayesian goodness-of-link test for testing logit link versus probit link is considered. Based upon the well known facts that cdf of logistic variate .approx. cdf of $t_{8}$/.634 and, as .nu. .to. .infty., cdf of $t_{\nu}$ approximates to that of N(0,1), Bayes factor is derived as a test criterion. A synthesis of the Gibbs sampling and a marginal likelihood estimation scheme is also proposed to compute the Bayes factor. Performance of the test is investigated via Monte Carlo study. The new test is also illustrated with an empirical data example.e.

• Journal of Labour Economics
• /
• v.28 no.1
• /
• pp.135-156
• /
• 2005

This paper analyzes the distribution and determinants of income of the self-employed, in comparison with salaried workers. Relative to salaried workers, in general, the self-employed tend to have a larger dispersion of income and larger heterogeneity. In this regard, the quantile regression analysis was used, along with a typical OLS regression analysis. According to the empirical findings, the income of the self-employed is larger than that of salaried workers, and this difference is larger for higher income group. The marginal effect of education is larger for higher income groups for both the self-employed and salaried workers, implying the return on education is larger for higher income groups. In contrast, for self-employed women, the marginal effect of education is smaller for higher income groups. Put differently, the return on education in the labor market is larger for salaried workers and self-employed men of high income groups as compared to those of low income groups, whereas the opposite holds for self-employed women.

• The Korean Journal of Applied Statistics
• /
• v.26 no.3
• /
• pp.389-399
• /
• 2013

This article introduces Gaussian process regression and shows its application with time-course microarray gene expression data. Gene screening for yeast cell cycle microarray expression data is accomplished with a ratio of log marginal likelihood that uses Gaussian process regression with a squared exponential covariance kernel function. Gaussian process regression fitting with each gene is done and shown with the nine top ranking genes. With the screened data the Gaussian model-based clustering is done and its silhouette values are calculated for cluster validity.