• 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.

• 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.

• 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.

• 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.

• 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.

• Structural Engineering and Mechanics
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• v.84 no.4
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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.

• Steel and Composite Structures
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• v.4 no.3
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• pp.211-226
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• 2004

This paper presents a study on the structural behaviour of modular steel scaffolds through both experimental and numerical investigations. Three one-storey and three two-storey modular steel scaffolds were built and tested to failure in order to examine the structural behaviour of typical modular steel scaffolds. Details of the tests and their test results were presented in this paper. Moreover, an advanced non-linear analysis method was employed to evaluate the load carrying capacities of these scaffolds under different support conditions. Comparisons between the experimental and the numerical results on the structural behaviour of these modular steel scaffolds were also presented. Moreover, the restraining effects of external supports in practical situations were also studied through finite element methods. The predicted load carrying capacities and deformations at failure of these models under partially restrained conditions were found to be close to the experimental results. A codified design method for column buckling with modified slenderness ratios was adopted for practical design of modular steel scaffolds.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.3
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• pp.331-343
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• 2002

This paper proposes a fuzzy rule-based model as a means to build usability models between emotional satisfaction and design variables of consumer products. Based on a subtractive clustering algorithm, this model obtains partially overlapping rules from existing data and builds multiple local models each of which has a form of a linear regression equation. The best subset procedure and cross validation technique are used to select appropriate input variables. The proposed technique was applied to the modeling of luxuriousness, balance, and attractiveness of office chairs. For comparison, regression models were built on the same data in two different ways; one using only potentially important variables selected by the design experts, and the other using all the design variables available. The results showed that the fuzzy rule-based model had a great benefit in terms of the number of variables included in the model. They also turned out to be adequate for predicting the usability of a new product. Better yet, the information on the product classes and their satisfaction levels can be obtained by interpreting the rules. The models, when combined with the information from the regression models, are expected to help the designers gain valuable insights in designing a new product.

• The Mathematical Education
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• v.50 no.1
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• pp.103-118
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• 2011

The study examined whether the relation between mathematics self-efficacy and mathematics achievement was partially mediated by the learning strategies, using latent growth model analyses. It was also examined the auto-regressive, cross-lagged (ARCL) panel model for testing the stability and change in the relation of mathematics self-efficacy and learning strategy over time. The study analyzed the first-year to the third-year data of the Korean Educational Longitudinal Survey (KELS). The result of ARCL panel model analysis showed that earlier mathematics self-efficacy could predict later learning strategy use. There were linear trends in mathematics self-efficacy, learning strategy, and mathematics achievement. Specifically, mathematics achievement was increased over the three time points, whereas mathematics self-efficacy and learning strategies were significantly decreased. In the analyses of latent growth models, the mediating effects of learning strategies were overall supported. That is, both of initial status and change rate of rehearsal strategy partially mediated the relation of mathematics self-efficacy and mathematics achievement. However, in elaboration and meta-cognitive strategies, only the initial status of each variable showed the indirect relationship.

• Korean journal of food and cookery science
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• v.26 no.5
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• pp.575-585
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• 2010

This study was performed to determine the optimal composition of a muffin administered dried burdock powder and oligosaccharide. The experiment was designed base on CCD (Central Composite Design), and evaluation was carried out by means of RSM (Response Surface Methodology), which included 10 experimental points with 3 replicates for the two independent variables burdock powder and oligosaccharide. The experimental muffin was made according to a traditional recipe, except that the flour was partially replaced with dried burdock powder (5%, 15%, 25%) and the sugar was partially replaced with oligosaccharide (25%, 50%, 75%). The compositional and functional properties of the prepared products were measured, and these values were applied to the mathematical models. Using the F-test, volume, height, pH, yellowness, chewiness, resilience, springiness, cohesiveness, taste, and overall quality were expressed as a linear model, whereas lightness, redness, adhesiveness, color, flavor, and overall quality were expressed as a quadratic model. The polynomial models developed by RSM for sensory evaluation, color, flavor, texture, taste, and overall quality were highly effective in describing the relationships between the factors (p<0.01). The estimated response surfaces confirmed that the amount of burdock powder had significant effects on color, taste, texture, flavor, and overall quality (p<0.01), whereas and the amount of oligosaccharide had significant effects on color and texture (p<0.01). Increased amount of burdock powder led to reductions of the sensory scores for color, taste, texture, flavor, and overall quality at all oligosaccharide levels. The optimal mixing percentage of burdock powder and oligosaccharide muffin were determined to be 5.00% and 46.25%, respectively.