• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.237-241
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• 2004

In standard time-to-event or survival analysis, the occurrence times of the event of interest are observed exactly or are right-censored. However in certain situations such as the AIDS data, the incubation period which is the time between HIV infection time and the diagnosis of AIDS is usually doubly censored. That is the HIV infection time Is interval censored and also the time of the diagnosis of AIDS is right censored. In this paper, we Impute the Interval censored infection time using the conditional mean imputation and estimate the coefficient factor of the regression analysis for the incubation period using Gibbs sampler. We applied parametric and semi-parametric methods for the analysis of the Incubation period and compared the results.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.227-232
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• 2003

Recently, support vector learning attracts an enormous amount of interest in the areas of function approximation, pattern classification, and novelty detection. One of the main reasons for the success of the support vector machines(SVMs) seems to be the availability of global and sparse solutions. Among the approaches sharing the same reasons for success and exhibiting a similarly good performance, we have KFD(kernel Fisher discriminant) approach. In this paper, we consider the problem of function approximation utilizing both predetermined basis functions and the KFD approach for regression. After reviewing support vector regression, semi-parametric approach for including predetermined basis functions, and the KFD regression, this paper presents an extension of the conventional KFD approach for regression toward the direction that can utilize predetermined basis functions. The applicability of the presented method is illustrated via a regression example.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.179-189
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• 2024

In multivariate regression with a high-dimensional response Y ∈ ℝ^r and a relatively low-dimensional predictor X ∈ ℝ^p (where r ≥ 2), the statistical analysis of such data presents significant challenges due to the exponential increase in the number of parameters as the dimension of the response grows. Most existing dimension reduction techniques primarily focus on reducing the dimension of the predictors (X), not the dimension of the response variable (Y). Yoo and Cook (2008) introduced a response dimension reduction method that preserves information about the conditional mean E(Y | X). Building upon this foundational work, Yoo (2018) proposed two semi-parametric methods, principal response reduction (PRR) and principal fitted response reduction (PFRR), then expanded these methods to unstructured principal fitted response reduction (UPFRR) (Yoo, 2019). This paper reviews these four response dimension reduction methodologies mentioned above. In addition, it introduces the implementation of the mbrdr package in R. The mbrdr is a unique tool in the R community, as it is specifically designed for response dimension reduction, setting it apart from existing dimension reduction packages that focus solely on predictors.

• Earthquakes and Structures
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• v.12 no.3
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• pp.321-332
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• 2017

The reverse fault is a dangerous geological hazard faced by buried steel pipelines. Permanent ground deformation along the fault trace will induce large compressive strain leading to buckling failure of the pipe. A hybrid pipe-shell element based numerical model programed by INP code supported by ABAQUS solver was proposed in this study to explore the strain performance of buried X80 steel pipeline under reverse fault displacement. Accuracy of the numerical model was validated by previous full scale experimental results. Based on this model, parametric analysis was conducted to study the effects of four main kinds of parameters, e.g., pipe parameters, fault parameters, load parameter and soil property parameters, on the strain demand. Based on 2340 peak strain results of various combinations of design parameters, a semi-empirical model for strain demand prediction of X80 pipeline at reverse fault crossings was proposed. In general, reverse faults encountered by pipelines are involved in 3D oblique reverse faults, which can be considered as a combination of reverse fault and strike-slip fault. So a compressive strain demand estimation procedure for X80 pipeline crossing oblique-reverse faults was proposed by combining the presented semi-empirical model and the previous one for compression strike-slip fault (Liu 2016). Accuracy and efficiency of this proposed method was validated by fifteen design cases faced by the Second West to East Gas pipeline. The proposed method can be directly applied to the strain based design of X80 steel pipeline crossing oblique-reverse faults, with much higher efficiency than common numerical models.

• Steel and Composite Structures
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• v.8 no.5
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• pp.403-421
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• 2008

The design provisions for semi-rigid steel frames have been incorporated in codes of practice for steel structures. In order to do the same, it is necessary to know the experimental moment-relative rotation (M-${\theta}_r$) behaviour of beam-to-column connections. In spite of numerous publications and collection of several connection databases, there is no unified approach for the semi-rigid design of steel frames. Amongst the many connection models available, the Frye-Morris polynomial model, with its limitations reported in the literature, is simple to adopt at least for the linear design space. However this model requires more number of connection tests and regression analyses to make it a realistic prediction model. In this paper, 3D nonlinear finite element (FE) analysis of beam-column connection specimens, carried out using ABAQUS software, for evaluating the M-${\theta}_r$ behaviour of semi-rigid top and seat-angle (TSA) bolted connections are described. The finite element model is validated against experimental behaviour of the same connection with regard to their moment-rotation behaviour, stress distribution and mode of failure of the connections. The calibrated FE model is used to evaluate the performance of the Frye-Morris polynomial model. The results of the numerical parametric studies carried out using the validated FE model have been used in proposing modifications to the Frye-Morris model for TSA connection in terms of the powers of the size parameters.

• Journal of Korean Society of Steel Construction
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• v.13 no.3
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• pp.289-299
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• 2001

The purpose of this study was to analyze the characteristics of semi-igid behavior of the steel tubular column to H-beam connection reinforced with exterior square-plate diaphragms and to check the main parameters that affect this behavior. Steel tube connections without interior diaphragm and/or complicated exterior diaphragm show the considerable flexibility due to out of-plane deformation of tube flange. For the exact analysis well-reflected the effect of this flexibility on the overall frame performance. it need to find out the moment-rotation curve function that well trace the result of experiment in the whole region and the function should be simply transformed into an adequate form for the nonlinear analysis program. After collecting several test data same to the connection type considered. we carried out FEM analysis using ANSYS for the assumed beam-to-column connection developed from the simple tension test and the results are compared with experimental values. Based on the parametric study. we proposed the moment-relation curve function and performed the multiple-regression analysis procedure for three parameters consisting of this function with the main geometric parameter of this connection type.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.965-976
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• 2015

Determining relevant variables for a regression model is important in regression analysis. Recently, a variable selection methods using a penalized likelihood with various penalty functions (e.g. LASSO and SCAD) have been widely studied in simple statistical models such as linear models and generalized linear models. The advantage of these methods is that they select important variables and estimate regression coefficients, simultaneously; therefore, they delete insignificant variables by estimating their coefficients as zero. We study how to select proper variables based on penalized hierarchical likelihood (HL) in semi-parametric frailty models that allow three penalty functions, LASSO, SCAD and HL. For the variable selection we develop a new function in the "frailtyHL" R package. Our methods are illustrated with breast cancer survival data from the Medical Center at Chonnam National University in Korea. We compare the results from three variable-selection methods and discuss advantages and disadvantages.

• Geotechnical Engineering
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• v.13 no.6
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• pp.107-122
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• 1997

This paper presents a ground surface settlement prediction method for shield tunneling in cohesive soils. In order to develop the method, a parametric study on shield tunneling was performed by using a threetimensional elasto-plastic finite element analysis, which can simulate the construction procedure. By using the results of the finite element analysis, the ground movement mechanism was investigated and a base which relates the ground surface settlement and iuluencing factors was formed. The data base was then used to formulate semi -empirical equations for both surface settlement ratio above tunnel face and imflection point by means of a regression analysis. Furthermore, a prediction method for transverse and longitudinal surface settlement profiles was suggested by using the leveloped equations in conjunction with the normal probability curve. Effectiveness of the developed method was illustrated by comparing settlement profiles obtained by using the developed method with the results of finite element analysis and measured data. Based on the comparison, it was concluded that the developed method can be effectively rosed for practical applications at least within the conditions investigated.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.1-10
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• 2023

The nuclear power plant(NPP)'s Instrumentation and Control(I&C) system periodically conducts integrity checks for the maintenance of self-diagnostic function during normal operation. Additionally, it performs functionality and performance checks during planned preventive maintenance periods. However, there is a need for technological development to diagnose failures and prevent accidents in advance. In this paper, we studied methods for estimating the reliability function by utilizing environmental data and self-diagnostic data of the I&C equipment. To obtain failure data, we assumed probability distributions for component features of the I&C equipment and generated virtual failure data. Using this failure data, we estimated the reliability function using representative artificial intelligence(AI) models used in survival analysis(DeepSurve, DeepHit). And we also estimated the reliability function through the Cox regression model of the traditional semi-parametric method. We confirmed the feasibility through the residual lifetime calculations based on environmental and diagnostic data.