• Journal of Microbiology
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• v.45 no.6
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• pp.572-577
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• 2007

To enhance therapeutic potential of murine monoclonal antibody, humanization by CDR grafting is usually used to reduce immunogenic mouse residues. Most humanized antibodies still have mouse residues critical for antigen binding, but the mouse residues may evoke immune responses in humans. Previously, we constructed a new humanized version (AKA) of mouse CC49 antibody specific for tumor-associated glycoprotein, TAG-72. In this study, to select a completely human antibody light chain against TAG-72, guided selection strategy using phage display was used. The heavy chain variable region (VH) of AKA was used to guide the selection of a human TAG-72-specific light chain variable region (VL) from a human VL repertoire constructed from human PBL. Most of the selected VLs were identified to be originated from the members of the human germline VK1 family, whereas the VL of AKA is more homologous to the VK4 family. Competition binding assay of the selected Fabs with mouse CC49 suggested that the epitopes of the Fabs overlap with that of CC49. In addition, they showed better antigen-binding affinity compared to parental AKA. The selected human VLs may be used to guide the selection of human VHs to get completely human anti-TAG72 antibody.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.23 no.2
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• pp.198-209
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• 1999

This study reports the selection of dependent variables for momentum equations in general curvilinear coordinates. Catesian, covariant and contravariant velocity components were examined for the dependent variable. The focus of present study is confined to staggered grid system Each dependent variable selected for momentum equations are tested for several flow fields. Results show that the selection of Cartesian and covariant velocity components intrinsically can not satisfy mass conservation of control volume unless additional converting processes ore used. Also, Cartesian component can only be used for the flow field in which main-flow direction does not change significantly. Convergence rate for the selection of covariant velocity component decreases quickly as with the increase of non-orthogonality of grid system. But the selection of contravariant velocity component reduces the total mass residual of discretized equations rapidly to the limit of machine accuracy and the solutions are insensitive to the main-flow direction.

• Journal of Korea Technology Innovation Society
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• v.14 no.1
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• pp.22-39
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• 2011

This study examines the wage level from a viewpoint of major-job match as part of an analysis on the skill mismatch problem in 4-year college graduates. The empirical analysis explicitly incorporate the sample selection bias as an econometric problem not only suggested but merely introduced in the earlier studies. This study also set up a major-job match variable, which was usually handled as a binary variable for analytical convenience, as a polychotomous choice variable in selection equation as provided by the survey. In particular, it considered multi-cohort survey on graduates of the years 1982, 1992, and 2002 for the empirical analysis. As a result of empirical analysis, the wage premium of a major-job match was identified. This result was consistent after the consideration of a sample selection bias and also after modeling the major-job match variable as polychotomously selective. Through an analysis classified by the major, this study identified a relatively high wage premium among Social Science, Engineering, and Science majors. However, there was a difference in the effect of selection among these majors. Also, by assessing cohort effects this study found that the skill mismatch had rapidly progressed in 1992, while difference between 1992 and 2002 cohorts are insignificant. The analysis suggests that wage level is better understood within the context of both sample selection and major-job match, and regardless of model specification the major-job match affects wage strongly.

• The Korean Journal of Applied Statistics
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• v.23 no.1
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• pp.207-218
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• 2010

Generalized additive model(GAM) is the statistical model that resolves most of the problems existing in the traditional linear regression model. However, overfitting phenomenon can be aroused without applying any method to reduce the number of independent variables. Therefore, variable selection methods in generalized additive model are needed. Recently, Lasso related methods are popular for variable selection in regression analysis. In this research, we consider Group Lasso and Elastic net models for variable selection in GAM and propose an algorithm for finding solutions. We compare the proposed methods via Monte Carlo simulation and applying auto insurance data in the fiscal year 2005. lt is shown that the proposed methods result in the better performance.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1213-1224
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• 2011

In high dimensionality where the number of variables are excessively larger than observations, it is required to remove the noninformative variables to cluster observations. Most model-based approaches for variable selection have been considered under the assumption of homoscedasticity and their models are mainly estimated by a penalized likelihood method. In this paper, a different approach is proposed to remove the noninformative variables effectively and to cluster based on the modified normal mixture model simultaneously. The validity of the model was provided and an EM algorithm was derived to estimate the parameters. Simulation studies and an experiment using real microarray dataset showed the effectiveness of the proposed method.

• IE interfaces
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• v.18 no.3
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• pp.253-267
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• 2005

Customer satisfaction becomes one of the important managerial concerns associated with corporate competency in current competitive environment for Internet communication service companies. Hence, it is demanding to improve a company's customer satisfaction through the total quality management perspective. In practice, engineers as well as the management hope to find major quality characteristics with Internet communication network that is closely related to customer satisfaction, consequently aiming to the raise of their company's customer satisfaction. This paper presents an empirical relationship analysis between network quality characteristics and customer satisfaction on Internet communication. Methodologically, the relationship analysis framework is based on the regression variable selection procedures. In this framework, it is implemented that; 1) iterative model building; and 2) consistent criteria application to statistical tests for selecting significant variables. A case study shows that; 1) the customer satisfaction on the network connection seems to be more closely related to the network quality characteristics compared with the customer satisfaction on the network speed; and 2) the download disconnection rate has relatively evident relationship with the customer satisfaction on the network connection.

• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.633-642
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• 2008

Variable selection algorithm for partial least square regression using penalty function is proposed. We use the fact that usual partial least square regression problem can be expressed as a maximization problem with appropriate constraints and we will add penalty function to this maximization problem. Then simulated annealing algorithm can be used in searching for optimal solutions of above maximization problem with penalty functions added. The HARD penalty function would be suggested as the best in several aspects. Illustrations with real and simulated examples are provided.

• Journal of the Korean Data and Information Science Society
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• v.24 no.5
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• pp.1077-1088
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• 2013

High-dimensional data analysis arises from almost all scientific areas, evolving with development of computing skills, and has encouraged penalized estimations that play important roles in statistical learning. For the past years, various penalized estimations have been developed, and the least absolute shrinkage and selection operator (LASSO) proposed by Tibshirani (1996) has shown outstanding ability, earning the first place on the development of penalized estimation. In this paper, we first introduce a number of recent advances in high-dimensional data analysis using the LASSO. The topics include various statistical problems such as variable selection and grouped or structured variable selection under sparse high-dimensional linear regression models. Several unsupervised learning methods including inverse covariance matrix estimation are presented. In addition, we address further studies on new applications which may establish a guideline on how to use the LASSO for statistical challenges of high-dimensional data analysis.

• Journal of the Korean Home Economics Association
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• v.37 no.12 s.142
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• pp.13-28
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• 1999

This study aimed to analyze the process of courtship form having date and marriage toward women who had job before marriage. The objects were 27.36 years old and had 9.59 months marital life at average. This study conducted structured questionnaires using the reflection of spouse selection procedure. By using collected data through questionnaires, it conducted descriptive statistics, cluster analysis and t-test. The type of marriage would be divided into the emotional marriage group and the implemental marriage group The emotional marriage group had longer dating time and more satisfaction of spouse selection than the implemental group. On value variable, the emotional marriage group had more subjective selection standard and more expectation social and emotional benefit than the latter group. On search variable, the emotional marriage group estimated the relation stability and satisfied the relation with spouse more than the implemental marriage group and expected the less possibility to meet new partner.

• Journal of the Korean Statistical Society
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• v.28 no.2
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• pp.167-182
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• 1999

This article is concerned with the selection of subsets of predictor variables to be included in building the binary response probit regression model. It is based on a Bayesian approach, intended to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets. This procedure reformulates the probit regression setup in a hierarchical normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. The appropriate posterior probability of each subset of predictor variables is obtained through the Gibbs sampler, which samples indirectly from the multinomial posterior distribution on the set of possible subset choices. Thus, in this procedure, the most promising subset of predictors can be identified as the one with highest posterior probability. To highlight the merit of this procedure a couple of illustrative numerical examples are given.