• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.1
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• pp.87-94
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• 2017

Reliability analysis(RA) and Reliability-based design optimization(RBDO) require statistical modeling of input random variables, which is parametrically or nonparametrically determined based on experimental data. For the parametric method, goodness-of-fit (GOF) test and model selection method are widely used, and a sequential statistical modeling method combining the merits of the two methods has been recently proposed. Kernel density estimation(KDE) is often used as a nonparametric method, and it well describes a distribution function when the number of data is small or a density function has multimodal distribution. Although accurate statistical models are needed to obtain accurate RA and RBDO results, accurate statistical modeling is difficult when the number of data is small. In this study, the accuracy of two statistical modeling methods, SSM and KDE, were compared according to the number of data. Through numerical examples, the RA results using the input models modeled by two methods were compared, and appropriate modeling method was proposed according to the number of data.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.11
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• pp.6751-6755
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• 2013

Background: Research on cancers with a high rate of mortality such as those occurring in the stomach requires using models which can provide a closer examination of disease processes and provide researchers with more accurate data. Various models have been designed based on this issue and the present study aimed at evaluating such models. Materials and Methods: Data from 330 patients with gastric cancer undergoing surgery at Iran Cancer Institute from 1995 to 1999 were analyzed. Cox-Snell Residuals and Akaike Information Criterion were used to compare parametric and semi-parametric Cox models in modeling transition rates among different states of a multi-state model. R 2.15.1 software was used for all data analyses. Results: Analysis of Cox-Snell Residuals and Akaike Information Criterion for all probable transitions among different states revealed that parametric models represented a better fitness. Log-logistic, Gompertz and Log-normal models were good choices for modeling transition rate for relapse hazard (state $1{\rightarrow}state$ 2), death hazard without a relapse (state $1{\rightarrow}state$ 3) and death hazard with a relapse (state $2{\rightarrow}state$ 3), respectively. Conclusions: Although the semi-parametric Cox model is often used by most cancer researchers in modeling transition rates of multistate models, parametric models in similar situations- as they do not need proportional hazards assumption and consider a specific statistical distribution for time to occurrence of next state in case this assumption is not made - are more credible alternatives.

• Journal of the Korean Geographical Society
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• v.51 no.2
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• pp.219-233
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• 2016

In social science fields, statistical models are used almost exclusively for causal explanation, and explanatory modeling has been a mainstream until now. In contrast, predictive modeling has been rare in the fields. Hence, we focus on constructing the predictive non-parametric model, instead of the explanatory model. Gangnam-gu, Seoul was chosen as a study area and we collected single-family house sales data sold between 2011 and 2014. We applied non-parametric models proposed in machine learning area including generalized additive model(GAM), random forest, multivariate adaptive regression splines(MARS) and support vector machines(SVM). Models developed recently such as MARS and SVM were found to be superior in predictive power for house price estimation. Finally, spatial autocorrelation was accounted for in the non-parametric models additionally, and the result showed that their predictive power was enhanced further. We hope that this study will prompt methodology for property price estimation to be extended from traditional parametric models into non-parametric ones.

• Journal of Welding and Joining
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• v.32 no.3
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• pp.1-10
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• 2014

The efficient application of any welding process depends on the understanding of associated process parameters influence on the weld quality. The weld quality includes the weld bead dimensions, temperature distribution, metallurgical phases and the mechanical properties. A detailed review on the experimental and numerical approaches to understand the parametric influence of a single wire submerged arc welding (SAW) and multi-wire SAW processes on the final weld quality is reported in two parts. The first part deals with the experimental approaches which explain the parametric influence on the weld bead dimensions, metallurgical phases and the mechanical properties of the SAW weldment. Furthermore, the studies related to statistical modeling of the present welding process are also discussed. The second part deals with the numerical approaches which focus on the conduction based, and heat transfer and fluid flow analysis based studies in the present welding process. The present paper is the first part.

• Structural Engineering and Mechanics
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• v.84 no.2
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• pp.207-224
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• 2022

The mechanical behavior of prestressed concrete haunched beams (PSHBs) was investigated in depth using a finite element modeling technique in this study. The efficiency of finite element modeling was investigated in the first stage by taking into account a previous study from the literature. The first stage's findings suggested that finite element modeling might be preferable for modeling PSHBs. In the second stage of the research, a comprehensive parametric study was carried out to determine the effect of each parameter on PSHB load capacity, including haunch angle, prestress level, compressive strength, tensile reinforcement ratio, and shear span to depth ratio. PSHBs and prestressed concrete rectangular beams (PSRBs) were also compared in terms of capacity. Stochastic analysis was used in the third stage to define the uncertainty in PSHB capacity by taking into account uncertainty in geometric and material parameters. Standard deviation, coefficient of variation, and the most appropriate probability density function (PDF) were proposed as a result of the analysis to define the randomness of capacity of PSHBs. In the study's final section, a new equation was proposed for using symbolic regression to predict the load capacity of PSHBs and PSRBs. The equation's statistical results show that it can be used to calculate the capacity of PSHBs and PSRBs.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.1358-1365
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• 2010

Estimating condition of slope is difficult because of nonlinear time dependency and seasonal effects, which affect the displacements. Displacements and displacement patterns of landslides are highly variable in time and space, and a unique approach cannot be defined to model landslide movements. Characteristics of movements are obtained by using a statistical method called Principal Component Analysis(PCA). The PCA is a non-parametric method to separate unknown, statistically uncorrelated source processes from observed mixed processes. In the non-parametric approaches, no physical assumptions of target systems are required. Instead, since the "best" mathematical relationship is estimated for given data sets of the input and output measured from target systems. As a consequence, non-parametric approaches are advantageous in modeling systems whose geomechanical properties are unknown or difficult to be measured. Non-parametric approaches are consequently more flexible in modeling than parametric approaches. This method is expected to be a useful tool for the slope management of and alarm systems.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.10
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• pp.1139-1145
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• 2014

Creation of a human skeleton model and characterization of the variation in the bone shape are fundamentally important in many applications of biomechanics. In this paper, we present a parametric shape modeling method for femurs that is based on extracting the main parameter of variations of the femur shape from a 3D model database by using statistical shape analysis. For this shape analysis, principal component analysis (PCA) is used. Application of the PCA to 3D data requires bringing all the models in correspondence to each other. For this reason, anatomical landmarks are used for guiding the deformation of the template model to fit the 3D model data. After subsequent application of PCA to a set of femur models, we calculate the correlation between the dominant components of shape variability for a target population and the anatomical parameters of the femur shape. Finally, we provide tools for visualizing and creating the femur shape using the main parameter of femur shape variation.

• Journal of the Korean Statistical Society
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• v.31 no.1
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• pp.77-91
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• 2002

This paper considers a functional regression model with truncated errors in explanatory variables. We show that the ordinary least squares (OLS) estimators produce bias in regression parameter estimates under misspecified models with ignored errors in the explanatory variable measurements, and then propose methods for analyzing the functional model. Fully parametric frequentist approaches for analyzing the model are intractable and thus Bayesian methods are pursued using a Markov chain Monte Carlo (MCMC) sampling based approach. Necessary theories involved in modeling and computation are provided. Finally, a simulation study is given to illustrate and examine the proposed methods.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.179-182
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• 2003

This study describes a new graphical method for assessing and characterizing effect modification by a matching covariate in matched case-control studies. This method to understand effect modification is based on a semiparametric model using a varying coefficient model. The method allows for nonparametric relationships between effect modification and other covariates, or can be useful in suggesting parametric models. This method can be applied to examining effect modification by any ordered categorical or continuous covariates for which cases have been matched with controls. The method applies to effect modification when causality might be reasonably assumed. An example from veterinary medicine is used to demonstrate our approach. The simulation results show that this method, when based on linear, quadratic and nonparametric effect modification, can be more powerful than both a parametric multiplicative model fit and a fully nonparametric generalized additive model fit.

• Structural Engineering and Mechanics
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• v.66 no.4
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• pp.423-438
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• 2018

This pioneer study focuses on finite element modeling and numerical modeling of three types of Reinforced Concrete Haunched Beams (RCHBs). Firstly, twenty RCHBs, consisting of three types, and four prismatic beams which had been tested experimentally were modeled via a nonlinear finite element method (NFEM) based software named as, ATENA. The modeling results were compared with experimental results including load capacity, deflection, crack pattern and mode of failure. The comparison showed a good agreement between the results and thus the model used can be effectively used for further studies of RCHB with high accuracy. Afterwards, new mechanism modes and design code equations were proposed to improve the shear design equation of ACI-318 and to predict the critical effective depth. These equations are the first comprehensive formulas in the literature involving all types of RCHBs. The statistical analysis showed the superiority of the proposed equation to their predecessors where the correlation coefficient, $R^2$ was found to be 0.89 for the proposed equation. Moreover, the new equation was validated using parametric and reliability analyses. The parametric analysis of both experimental and predicted results shows that the inclination angle and the compressive strength were the most influential parameters on the shear strength. The reliability analysis indicates that the accuracy of the new formulation is significantly higher as compared to available design equations and its reliability index is within acceptable limits.