• Communications for Statistical Applications and Methods
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• 제22권3호
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• pp.305-312
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• 2015

We extend a generalized partially linear single-index model and newly define a generalized partially double-index model (GPDIM). The philosophy of sufficient dimension reduction is adopted in GPDIM to estimate unknown coefficient vectors in the model. Subsequently, various combinations of popular sufficient dimension reduction methods are constructed with the best combination among many candidates determined through a bootstrapping procedure that measures distances between subspaces. Distinguishing values are newly defined to match the estimates to the corresponding population coefficient vectors. One of the strengths of the proposed model is that it can investigate the appropriateness of GPDIM over a single-index model. Various numerical studies confirm the proposed approach, and real data application are presented for illustration purposes.

• Communications for Statistical Applications and Methods
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• 제26권1호
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• pp.69-78
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• 2019

A structured model with both single-index and varying coefficients is a powerful tool in modeling high dimensional data. It has been widely used because the single-index can overcome the curse of dimensionality and varying coefficients can allow nonlinear interaction effects in the model. For high dimensional index vectors, variable selection becomes an important question in the model building process. In this paper, we propose an efficient estimation and a variable selection method based on a smoothing spline approach in a partially linear single-index-coefficient regression model. We also propose an efficient algorithm for simultaneously estimating the coefficient functions in a data-adaptive lower-dimensional approximation space and selecting significant variables in the index with the adaptive LASSO penalty. The empirical performance of the proposed method is illustrated with simulated and real data examples.

• Journal of the Korean Data and Information Science Society
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• 제26권1호
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• pp.209-216
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• 2015

Quantile regression models with covariate measurement errors have received a great deal of attention in both the theoretical and the applied statistical literature. A lot of effort has been devoted to develop effective estimation methods for such quantile regression models. In this paper we propose the partially linear support vector orthogonal quantile regression model in the presence of covariate measurement errors. We also provide a generalized approximate cross-validation method for choosing the hyperparameters and the ratios of the error variances which affect the performance of the proposed model. The proposed model is evaluated through simulations.

• 응용통계연구
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• 제24권4호
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• pp.587-595
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• 2011

Credit scoring is an objective and automatic system to assess the credit risk of each customer. The logistic regression model is one of the popular methods of credit scoring to predict the default probability; however, it may not detect possible nonlinear features of predictors despite the advantages of interpretability and low computation cost. In this paper, we propose to use a generalized partially linear model as an alternative to logistic regression. We also introduce modern ensemble technologies such as bagging, boosting and random forests. We compare these methods via a simulation study and illustrate them through a German credit dataset.

• Communications for Statistical Applications and Methods
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• 제19권6호
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• pp.809-817
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• 2012

We propose a semiparametric inference for a generalized varying coefficient partially linear model(VCPLM) for negative binomial data. The VCPLM is useful to model real data in that varying coefficients are a special type of interaction between explanatory variables and partially linear models fit both parametric and nonparametric terms. The negative binomial distribution often arise in modelling count data which usually are overdispersed. The varying coefficient function estimators and regression parameters in generalized VCPLM are obtained by formulating a penalized likelihood through smoothing splines for negative binomial data when the shape parameter is known. The performance of the proposed method is then evaluated by simulations.

• Communications for Statistical Applications and Methods
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• 제27권5호
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• pp.511-521
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• 2020

In this paper, a partially linear multivariate model with error in the explanatory variable of the nonparametric part, and an m dimensional response variable is considered. Using the uniform consistency results found for the estimator of the nonparametric part, we derive an estimator of the parametric part. The dependence of the convergence rates on the errors distributions is examined and demonstrated that proposed estimator is asymptotically normal. In main results, both ordinary and super smooth error distributions are considered. Moreover, the derived estimators are applied to the economic behaviors of consumers. Our method handles contaminated data is founded more effectively than the semiparametric method ignores measurement errors.

• Journal of the Korean Statistical Society
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• 제32권1호
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• pp.85-92
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• 2003

We consider the partially linear model E(Y) : X$^{t}$ $\beta$＋η(Z) when the X's are measured with additive error. The semiparametric likelihood estimation ignoring the measurement error gives inconsistent estimator for both $\beta$ and η(.). In this paper we suggest the SIMEX estimator for f to correct the bias induced by measurement error, and explore its properties. We show that the rational linear extrapolant is proper in extrapolation step in the sense that the SIMEX method under this extrapolant gives consistent estimator It is also shown that the SIMEX estimator is asymptotically equivalent to the semiparametric version of the usual parametric correction for attenuation suggested by Liang et al. (1999) A simulation study is given to compare two variance estimating methods for SIMEX estimator.

• 대한기계학회논문집
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• 제13권1호
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• pp.170-177
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• 1989

본 연구에서는 부분 포물형 Navier-Stokes 방정식에 Speziale이 제안한 비선형 ｋ-.epsilon. 난류 모형을 적용하여 비직교 조표계에서 전개 중인 유동의 평균 속도와 난류 운동에너지 등을 예측하였다.

• Structural Engineering and Mechanics
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• 제84권4호
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• pp.561-573
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• 2022

This paper presents a new model to obtain the minimum area of the contact surface for rectangular isolated footings, considering that the contact surface works partially to compression (a part of the contact surface of the footing is subjected to compression and the other is not in compression or tension). The methodology is developed by integration to obtain the axial load "P", moment around the X axis "M_x" and moment around the Y axis "M_y". This document presents the simplified and precise equations of the four possible cases of footing subjected to uniaxial bending and five possible cases of footing subjected to biaxial bending. The current model considers the contact area of the footing that works totally in compression, and other models consider the contact area that works partially under compression and these are developed by very complex iterative processes. Numerical examples are presented to obtain the minimum area of rectangular footings under an axial load and moments in two directions, and the results are compared with those of other authors. The results show that the new model presents smaller areas than the other authors presented.

• 한국전산구조공학회논문집
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• 제13권4호
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• pp.461-473
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• 2000

이 논문은 콘크리트의 크리프와 건조수축 현상을 고려한 강재와 콘크리트의 부분 합성보의 거동의 해석을 위한 모델을 제안하고 있다. 부분합성 거동은 선형 부분전단 연결이론을 토대로 합성보를 일정한 수의 요소로 분할한 후 각 절점에서 평형조건과 적합조건을 기초로하여 구성 방정식을 구성하고 경계조건과 각 요소에 대한 구성 방정식을 순차적으로 적용하는 방법으로 해석모델이 구성되었다. 또한 콘크리트의 시간에 따른 현상인 크리프와 건조수축의 영향도 고려되었다. 제안된 해석모델은 다경간 연속 합성보의 슬립거동을 효과적으로 나타낼 수 있다. 제안된 해석 모델의 검증을 위해 기존 연구의 결과와 비교되었으며, 여러 조건에 대한 합성보의 해석을 통해 제안된 모델의 적용성을 입증하였다.