• Communications for Statistical Applications and Methods
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• 제7권3호
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• pp.817-827
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• 2000

In this paper, we investigate the Bayesian approach to random effect binomial regression models with improper prior due to the absence of information on parameter. We also propose a method of estimating the posterior moments and prediction and discuss some general methods for studying model assessment. The methodology is illustrated with Crowder's Seeds Data. Markov Chain Monte Carlo techniques are used to overcome the computational difficulties.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2005년도 춘계 학술발표회 논문집
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• pp.215-219
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• 2005

We consider penalized likelihood regression with exponential family responses. Parallel to recent development in Gaussian regression, the fast computation through asymptotically efficient low-dimensional approximations is explored, yielding algorithm that scales much better than the O($n^3$) algorithm for the exact solution. Also customizations of the direct cross-validation strategy for smoothing parameter selection in various distribution families are explored and evaluated.

• Journal of the Korean Data and Information Science Society
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• 제5권1호
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• pp.11-20
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• 1994

In recent years, nonparametric kernel estimation of regresion function are abundant and widely applicable to many areas of statistics. Most of modern researches concerned with the fixed global bandwidth selection which can be used in the estimation of regression function with all the same value for all x. In this paper, we propose a method for selecting locally varing bandwidth based on bootstrap method in kernel estimation of fixed design regression. Performance of proposed bandwidth selection method for finite sample case is conducted via Monte Carlo simulation study.

• Journal of the Korean Data and Information Science Society
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• 제10권1호
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• pp.163-172
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• 1999

We propose a cross-validatory method for the choice of the number of principal components in principal component regression based on the magnitudes of correlations with y. There are two different manners in choosing principal components, one is the order of eigenvalues(Shin and Moon, 1997) and the other is that of correlations with y. We apply our method to various data sets and compare results of those two methods.

• Journal of the Korean Data and Information Science Society
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• 제21권2호
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• pp.379-385
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• 2010

This paper considers Bayesian approach to modeling a flexible regression function under functional measurement error model. The regression function is modeled based on semiparametric regression with penalized splines. Model fitting and parameter estimation are carried out in a hierarchical Bayesian framework using Markov chain Monte Carlo methodology. Their performances are compared with those of the estimators under functional measurement error model without semiparametric component.

• Communications for Statistical Applications and Methods
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• 제8권1호
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• pp.15-27
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• 2001

One proposal is made for constructing nonparametric estimator of slope parameters in a regression model under symmetric error distributions. This estimator is based on the use of idea of Johns for estimating the center of the symmetric distribution together with the idea of regression quantiles and regression trimmed mean. This nonparametric estimator and some other L-estimators are studied by Monte Carlo.

• 응용통계연구
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• 제28권6호
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• pp.1133-1146
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• 2015

경마 산업은 국내 합법 사행산업의 대부분을 차지하고 있다. 그러나 사행성 도박이라는 인식 하에 여타 스포츠 산업에 비해 활발한 통계적 분석이 이루어지지 않고 있다. 본 연구의 목적은 다양한 데이터마이닝 기법을 이용하여 우승마를 예측하는 모형 개발에 있다. 모형 적합에 사용한 데이터는 한국 마사회에서 제공하는 자료를 바탕으로 하였으며, 경마 성적표, 경주마 정보, 기수 정보, 조교사 정보 등을 사용하였다. 예측 모형은 크게 두 모형으로 나누어 순위를 기반으로 한 모형과 기록을 기반으로 한 모형으로 적합하였고, 분석 방법으로는 선형회귀분석, 랜덤 포레스트, 로지스틱 회귀 분석을 사용하였다. 그 결과 말 기본 정보와 과거 우승 경력, 기수의 과거 우승 경력 등이 순위 예측에 큰 영향을 미치는 것을 알 수 있었다. 모형 적합에 사용되지 않은 최근 1개월 간 데이터를 이용하여 단승식, 복승식, 삼복승식으로 배팅한 결과 모형 간 큰 차이가 없었고, 모두 양의 수익을 얻을 수 있었다.

• 대한수학회논문집
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• 제31권1호
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• pp.185-198
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• 2016

Regression analysis is an analyzing method of regression model to explain the statistical relationship between explanatory variable and response variables. This paper propose a fuzzy regression analysis applying Theils method which is not sensitive to outliers. This method use medians of rate of increment based on randomly chosen pairs of each components of ${\alpha}$-level sets of fuzzy data in order to estimate the coefficients of fuzzy regression model. An example and two simulation results are given to show fuzzy Theils estimator is more robust than the fuzzy least squares estimator.

• Journal of the Korean Data and Information Science Society
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• 제27권2호
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• pp.531-538
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• 2016

In this paper we deal with the small area estimation for the case that the response variables take binary values. The mixed effects models have been extensively studied for the small area estimation, which treats the spatial effects as random effects. However, when the spatial information of each area is given specifically as coordinates it is popular to use the geographically weighted logistic regression to incorporate the spatial information by assuming that the regression parameters vary spatially across areas. In this paper, relaxing the linearity assumption and propose a geographically weighted kernel logistic regression for estimating small area proportions by using basic principle of kernel machine. Numerical studies have been carried out to compare the performance of proposed method with other methods in estimating small area proportion.

• Journal of the Korean Data and Information Science Society
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• 제28권5호
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• pp.1167-1177
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• 2017

The censored regression using the pseudo-response variable proposed by Buckley and James has been one of the most well-known models. Recently, the varying coefficient regression model has received a great deal of attention as an important tool for modeling. In this paper we propose a censored varying coefficient regression model using Buckley-James method to consider situations where the regression coefficients of the model are not constant but change as the smoothing variables change. By using the formulation of least squares support vector machine (LS-SVM), the coefficient estimators of the proposed model can be easily obtained from simple linear equations. Furthermore, a generalized cross validation function can be easily derived. In this paper, we evaluated the proposed method and demonstrated the adequacy through simulate data sets and real data sets.