• 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.

• Journal of the Korea Society for Simulation
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• v.10 no.4
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• pp.41-50
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• 2001

When optimizing a complex system by determining the optimum condition of the system parameters of interest, we often employ the process of estimating the unknown objective function, which is assumed to be a second order spline function. In doing so, we normally use common random numbers for different set of the controllable factors resulting in more accurate parameter estimation for the objective function. In this paper, we will show some mathematical result for the analysis of variance when using common random numbers in terms of the regression error, the residual error and the pure error terms. In fact, if we can realize the special structure of the covariance matrix of the error terms, we can use the result of analysis of variance for the uncorrelated experiments only by applying minor changes.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1199-1215
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• 2011

Relaxing an unrealistic assumption of a representative percolation model, this paper demonstrates that herd behavior leads to a high increase in volatility but not trading volume, in contrast with information flows that give rise to increases in both volatility and trading volume. Although detecting herd behavior has posed a great challenge due to its empirical difficulty, this paper proposes a new methodology for detecting trading days with herding. Furthermore, this paper suggests a herd-behavior-stochastic-volatility model, which accounts for herding in financial markets. Strong evidence in favor of the model specification over the standard stochastic volatility model is based on empirical application with high frequency data in the Korean equity market, strongly supporting the intuition that herd behavior causes excess volatility. In addition, this research indicates that strong persistence in volatility, which is a prevalent feature in financial markets, is likely attributed to herd behavior rather than news.

• Korean Journal of Community Nutrition
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• v.17 no.3
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• pp.329-340
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• 2012

Hypertension is a well-known risk factor for cardiovascular disease. Previous studies have shown that changes in diet and lifestyle factors can prevent the development of hypertension, but the combined effects of these modifiable factors on hypertension are not well established. The objective of this study is to investigate associations of diet and lifestyle factors, evaluated both individually and in combination, with prevalent hypertension among Korean adults. We analyzed data obtained from the 2007-2008 Korean National Health and Nutritional Examination Survey, a nationwide cross-sectional study using a stratified, multistage probability sampling design. The associations of 12 nutrient intakes and lifestyle factors with risk of hypertension were explored using restricted cubic spline regression and logistic regression models among 6,351 adults. Total energy and several nutrients and minerals, including, calcium, vitamin A, vitamin C, and sodium, showed non-linear relationships with the risk of prevalent hypertension. In multivariate logistic regression models, dietary score, obesity and alcohol intake were independently associated with the risk of prevalent hypertension, but smoking and physical activity were not. Overall, participants whose dietary habits and lifestyle factors were all in the low-risk group had 68% lower prevalence of hypertension (OR: 0.32, 95 CI: 0.14-0.74) compared to those who were at least one in the high-risk group of any dietary or lifestyle factors. The result suggests that combined optimal lifestyle habits are strongly associated with lower prevalence of hypertension among Korean adults.

• International Journal of Reliability and Applications
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• v.2 no.3
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• pp.209-240
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• 2001

Computer-based simulation and analysis is used extensively in engineering for a variety of tasks. Despite the steady and continuing growth of computing power and speed, the computational cost of complex high-fidelity engineering analyses and simulations limit their use in important areas like design optimization and reliability analysis. Statistical approximation techniques such as design of experiments and response surface methodology are becoming widely used in engineering to minimize the computational expense of running such computer analyses and circumvent many of these limitations. In this paper, we compare and contrast five experimental design types and four approximation model types in terms of their capability to generate accurate approximations for two engineering applications with typical engineering behaviors and a wide range of nonlinearity. The first example involves the analysis of a two-member frame that has three input variables and three responses of interest. The second example simulates the roll-over potential of a semi-tractor-trailer for different combinations of input variables and braking and steering levels. Detailed error analysis reveals that uniform designs provide good sampling for generating accurate approximations using different sample sizes while kriging models provide accurate approximations that are robust for use with a variety of experimental designs and sample sizes.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.13 no.3
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• pp.36-45
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• 2010

To analyze the relationship between climatic factors (monthly mean temperature and total precipitation) and tree-ring growths of Quercus mongolica Fischer (Mongolian oak) with different topographic sites in Mt. Worak, more than 10 trees were selected from each of seven stands. Two cores from each tree were measured for ring width. After crossdating, each ring-width series was double standardized by fitting first a negative exponential or straight regression line and secondly a 60-year cubic spline. Seven stands were categorized in two groups using cluster analysis for tree-ring index patterns. Cluster I (four stands) was located in higher elevation (550-812 m) with aspects of east, west and northwest, and cluster II (three stands) was located in rather lower election (330-628 m) with aspects of north and northwest. The aspects of two clusters were not significantly different. Response-function analysis showed a significant positive response to March precipitation for both clusters. It indicates that moisture supply during early spring season is important to radial growth because the cambial growths of ring-porous species, such as Mongolian oak, start before leaf growth. Cluster II showed a positive response to the precipitation of middle and late growing season, too.

• The Korean Journal of Applied Statistics
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• v.27 no.4
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• pp.605-617
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• 2014

Small area estimation is a statistical inference method to overcome large variance due to a small sample size allocated in a small area. A shrinkage estimator obtained by minimizing relative error(RE) instead of MSE has been suggested. The estimator takes advantage of good interpretation when the data range is large. A semiparametric estimator is also studied for small area estimation. In this study, we suggest a semiparametric shrinkage small area estimator and compare small area estimators using labor statistics.

• Computers and Concrete
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• v.27 no.4
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• pp.305-317
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• 2021

The bond between the concrete and bar is a main factor affecting the performance of the reinforced concrete (RC) members, and since the steel corrosion reduces the bond strength, studying the bond behavior of concrete and GFRP bars is quite necessary. In this research, a database including 112 concrete beam test specimens reinforced with spliced GFRP bars in the splitting failure mode has been collected and used to estimate the concrete-GFRP bar bond strength. This paper aims to accurately estimate the bond strength of spliced GFRP bars in concrete beams by applying three soft computing models including multivariate adaptive regression spline (MARS), Kriging, and M5 model tree. Since the selection of regularization parameters greatly affects the fitting of MARS, Kriging, and M5 models, the regularization parameters have been so optimized as to maximize the training data convergence coefficient. Three hybrid model coupling soft computing methods and genetic algorithm is proposed to automatically perform the trial and error process for finding appropriate modeling regularization parameters. Results have shown that proposed models have significantly increased the prediction accuracy compared to previous models. The proposed MARS, Kriging, and M5 models have improved the convergence coefficient by about 65, 63 and 49%, respectively, compared to the best previous model.

• Geomechanics and Engineering
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• v.33 no.3
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• pp.279-289
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• 2023

Brittleness index (BI) is an important property of rocks because it is a good index to predict rockburst. Due to its importance, several empirical and soft computing (SC) models have been proposed in the literature based on the punch penetration test (PPT) results. These models are very important as there is no clear-cut experimental means for measuring BI asides the PPT which is very costly and time consuming to perform. This study used a novel Multivariate Adaptive regression spline (MARS), M5P, and white-box ANN to predict the BI of rocks using the available data in the literature for an improved BI prediction. The rock density, uniaxial compressive strength (σ_c) and tensile strength (σ_t) were used as the input parameters into the models while the BI was the targeted output. The models were implemented in the MATLAB software. The results of the proposed models were compared with those from existing multilinear regression, linear and nonlinear particle swarm optimization (PSO) and genetic algorithm (GA) based models using similar datasets. The coefficient of determination (R²), adjusted R² (Adj R²), root-mean squared error (RMSE) and mean absolute percentage error (MAPE) were the indices used for the comparison. The outcomes of the comparison revealed that the proposed ANN and MARS models performed better than the other models with R² and Adj R² values above 0.9 and least error values while the M5P gave similar performance to those of the existing models. Weight partitioning method was also used to examine the percentage contribution of model predictors to the predicted BI and tensile strength was found to have the highest influence on the predicted BI.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.607-618
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• 2019

A new and interesting task in statistics is to effectively analyze functional data that frequently comes from advances in modern science and technology in areas such as meteorology and biomedical sciences. Functional linear regression with scalar response is a popular functional data analysis technique and it is often a common problem to determine a functional association if a functional predictor variable affects the scalar response in the models. Recently, Kong et al. (Journal of Nonparametric Statistics, 28, 813-838, 2016) established classical testing methods for this based on functional principal component analysis (of the functional predictor), that is, the resulting eigenfunctions (as a basis). However, the eigenbasis functions are not generally suitable for regression purpose because they are only concerned with the variability of the functional predictor, not the functional association of interest in testing problems. Additionally, eigenfunctions are to be estimated from data so that estimation errors might be involved in the performance of testing procedures. To circumvent these issues, we propose a testing method based on fixed basis such as B-splines and show that it works well via simulations. It is also illustrated via simulated and real data examples that the proposed testing method provides more effective and intuitive results due to the localization properties of B-splines.