• Journal of the Korean Data and Information Science Society
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• 제22권5호
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• pp.951-959
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• 2011

The proposed method is based on a penalized censored regression model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log likelihood function of censored regression model. It provide the efficient computation of regression parameters including variable selection and leads to the generalized cross validation function for the model selection. Numerical results are then presented to indicate the performance of the proposed method.

• Journal of the Korean Data and Information Science Society
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• 제23권6호
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• pp.1231-1239
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• 2012

An estimating procedure is introduced for kernel Poisson regression when the input variables consist of numerical and categorical variables, which is based on the penalized negative log-likelihood and the component-wise product of two different types of kernel functions. The proposed procedure provides the estimates of the mean function of the response variables, where the canonical parameter is linearly and/or nonlinearly related to the input variables. Experimental results are then presented which indicate the performance of the proposed kernel Poisson regression.

• 응용통계연구
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• 제34권2호
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• pp.279-293
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• 2021

본 논문은 선형 회귀모형에서 벌점 추정량의 신의 성질에 대한 충분조건을 구성하는 방법을 소개하였다. 신의 추정량, 벌점 추정량, 신의 벌점 추정량, 신의 성질을 명확히 정의하였으며 이를 바탕으로 신의 성질에 대한 최적조건과 최적조건에 대한 충분조건을 구성하는 방법을 대부분의 벌점함수에 적용 가능하도록 하나의 통합된 원리로 소개하였다. 추가로 신의 성질에 대한 이해를 돕기 위해 간단한 예제와 함께 가상실험 결과를 첨부하였다.

• Communications for Statistical Applications and Methods
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• 제22권2호
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• pp.147-157
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• 2015

We consider a sparse high-dimensional linear regression model. Penalized methods using LASSO or non-convex penalties have been widely used for variable selection and estimation in high-dimensional regression models. In penalized regression, the selection and prediction performances depend on which penalty function is used. For example, it is known that LASSO has a good prediction performance but tends to select more variables than necessary. In this paper, we propose an additive sparse penalty for variable selection using a combination of LASSO and minimax concave penalties (MCP). The proposed penalty is designed for good properties of both LASSO and MCP.We develop an efficient algorithm to compute the proposed estimator by combining a concave convex procedure and coordinate descent algorithm. Numerical studies show that the proposed method has better selection and prediction performances compared to other penalized methods.

• Communications for Statistical Applications and Methods
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• 제17권4호
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• pp.551-560
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• 2010

This paper considers a Bayesian approach to modeling a flexible regression function under structural 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 structural measurement error model without a semiparametric component.

• Journal of the Korean Data and Information Science Society
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• 제19권4호
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• pp.1327-1334
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• 2008

Mixed effect binomial regression models are widely used for analysis of correlated count data in which the response is the result of a series of one of two possible disjoint outcomes. In this paper, we consider kernel extensions with nonparametric fixed effects and parametric random effects. The estimation is 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 hyperparameters, cross-validation techniques are employed. Examples illustrating usage and features of the proposed method are provided.

• 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.

• Journal of the Korean Data and Information Science Society
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• 제18권3호
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• pp.767-772
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• 2007

A kernel machine is proposed as an estimating procedure for the linear and nonlinear Poisson regression, which is based on the penalized negative log-likelihood. The proposed kernel machine provides the estimate of the mean function of the response variable, where the canonical parameter is related to the input vector in a nonlinear form. The generalized cross validation(GCV) function of MSE-type is introduced to determine hyperparameters which affect the performance of the machine. Experimental results are then presented which indicate the performance of the proposed machine.

• 응용통계연구
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• 제28권6호
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• pp.1103-1111
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• 2015

본 논문에서는 상대오차의 개념과 벌점회귀를 결합한 새로운 예측방법을 제시하였다. 제안된 방법은 오차항의 분포가 정규성을 크게 벗어나 있어 이상점을 포함하거나 오차항의 분포가 심각하게 비대칭인 경우에도 안정적으로 예측력이 유지할 뿐 아니라 벌점회귀를 통한 변수선택의 성능도 우수하다. 또한 개념적으로 쉽고, 계산 속도가 빠르며, 기존의 알고리즘을 활용해 구현하는 것이 매우 쉽다. 한국교통연구원의 일일 차량통행량 자료 실제 분석 및 모의실험을 통해 제안된 방법의 우수한 성질을 확인하였다.

• Journal of the Korean Data and Information Science Society
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• 제26권6호
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• pp.1513-1521
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• 2015

Selecting relevant variables for a statistical model is very important in regression analysis. Recently, variable selection methods using a penalized likelihood have been widely studied in various regression models. The main advantage of these methods is that they select important variables and estimate the regression coefficients of the covariates, simultaneously. In this paper, we propose a simple procedure based on a penalized h-likelihood (HL) for variable selection in Poisson hierarchical generalized linear models (HGLMs) for correlated count data. For this we consider three penalty functions (LASSO, SCAD and HL), and derive the corresponding variable-selection procedures. The proposed method is illustrated using a practical example.