• Title, Summary, Keyword: partially linear

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GACV for partially linear support vector regression

  • Shim, Jooyong;Seok, Kyungha
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.2
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    • pp.391-399
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    • 2013
  • Partially linear regression is capable of providing more complete description of the linear and nonlinear relationships among random variables. In support vector regression (SVR) the hyper-parameters are known to affect the performance of regression. In this paper we propose an iterative reweighted least squares (IRWLS) procedure to solve the quadratic problem of partially linear support vector regression with a modified loss function, which enables us to use the generalized approximate cross validation function to select the hyper-parameters. Experimental results are then presented which illustrate the performance of the partially linear SVR using IRWLS procedure.

Coupled Finite Element Analysis for Semi-implicit Linear and Fully-implicit Nonlinear Scheme in Partially Saturated Porous Medium

  • Kim, Jae-Hong;Regueiro, Richard A.
    • LHI Journal of Land, Housing, and Urban Affairs
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    • v.1 no.1
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    • pp.59-65
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    • 2010
  • The paper presents a comparison between a semi-implicit time integration linear finite element implementation and fully-implicit nonlinear Newton-Raphson finite element implementation of a triphasic small strain mixture formulation of an elastic partially saturated porous medium. The pore air phase pressure pa is assumed atmospheric, i.e., $p_a$ = 0, although the formulation and implementation are general to handle increase in pore air pressure as a result of loading, if needed. The solid skeleton phase is assumed linear isotropic elastic and partially saturated 'consolidation' in the presence of surface infiltration and traction is simulated. The verification of the implementation against an analytical solution for partially saturated pore water flow (no deformation) and comparison between the two implementations is presented and the important of the porosity-dependent nature of the partially saturated permeability is assessed on comparison with a commercial code for the partially saturated flow with deformation. As a result, the response of partially saturated permeability subjected to the porosity influences on the saturation of a soil, and the different behaviors of the partially saturated soil between staggered and monolithic coupled programs is worth of attention because the negative pore water pressure in the partially saturated soil depends on the difference.

A Comparative Study on the Performance of Bayesian Partially Linear Models

  • Woo, Yoonsung;Choi, Taeryon;Kim, Wooseok
    • Communications for Statistical Applications and Methods
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    • v.19 no.6
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    • pp.885-898
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    • 2012
  • In this paper, we consider Bayesian approaches to partially linear models, in which a regression function is represented by a semiparametric additive form of a parametric linear regression function and a nonparametric regression function. We make a comparative study on the performance of widely used Bayesian partially linear models in terms of empirical analysis. Specifically, we deal with three Bayesian methods to estimate the nonparametric regression function, one method using Fourier series representation, the other method based on Gaussian process regression approach, and the third method based on the smoothness of the function and differencing. We compare the numerical performance of three methods by the root mean squared error(RMSE). For empirical analysis, we consider synthetic data with simulation studies and real data application by fitting each of them with three Bayesian methods and comparing the RMSEs.

Negative Binomial Varying Coefficient Partially Linear Models

  • Kim, Young-Ju
    • Communications for Statistical Applications and Methods
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    • v.19 no.6
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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.

Efficient estimation and variable selection for partially linear single-index-coefficient regression models

  • Kim, Young-Ju
    • Communications for Statistical Applications and Methods
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    • v.26 no.1
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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.

Generalized Partially Linear Additive Models for Credit Scoring

  • Shim, Ju-Hyun;Lee, Young-K.
    • The Korean Journal of Applied Statistics
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    • v.24 no.4
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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.

Partially linear support vector orthogonal quantile regression with measurement errors

  • Hwang, Changha
    • Journal of the Korean Data and Information Science Society
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    • v.26 no.1
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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.

Generalized Partially Double-Index Model: Bootstrapping and Distinguishing Values

  • Yoo, Jae Keun
    • Communications for Statistical Applications and Methods
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    • v.22 no.3
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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.

SOME PROPERTIES OF SIMEX ESTIMATOR IN PARTIALLY LINEAR MEASUREMENT ERROR MODEL

  • Meeseon Jeong;Kim, Choongrak
    • Journal of the Korean Statistical Society
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    • v.32 no.1
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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.