• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.651-662
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• 2017

We propose an alternative procedure to select penalty parameter in $L_1$ penalized robust regression. This procedure is based on marginalization of prior distribution over the penalty parameter. Thus, resulting objective function does not include the penalty parameter due to marginalizing it out. In addition, its estimating algorithm automatically chooses a penalty parameter using the previous estimate of regression coefficients. The proposed approach bypasses cross validation as well as saves computing time. Variable-wise penalization also performs best in prediction and variable selection perspectives. Numerical studies using simulation data demonstrate the performance of our proposals. The proposed methods are applied to Boston housing data. Through simulation study and real data application we demonstrate that our proposals are competitive to or much better than cross-validation in prediction, variable selection, and computing time perspectives.

• Journal of Korean Society for Quality Management
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• v.37 no.4
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• pp.1-9
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• 2009

Supplier selection process is one of the most important arenas of production and logistics management for many companies. This study explores the application of the analytic network process (ANP) approach for the evaluation of suppliers based on several different evaluation criteria. The ANP approach in this study is capable of providing priorities of suppliers that capture network relationships among several evaluation criteria which are not independent. Therefore this study provides value to practitioners by providing a generic model for supplier selection. In addition, for researchers, it demonstrates further research possibilities for more complex decision making problems using ANP.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.662-671
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• 2007

Since variety of construction projects with their individual specifications could be handled through different procurement systems, selection of the most appropriate project delivery system is a vital step towards more efficient project execution. The appropriate selection of project delivery system may also ensure more competent management of the project. Its impacts are not only limited to the first stages of the project, as it could also influence pre-construction, construction and operational phases of the project. Among different approaches exerted for this purpose, none has taken uncertainty into account, despite the fact that during first stages of the project most of the selection factors are still uncertain and not clearly defined. This paper, hence, aims to provide a fuzzy insight into the project delivery system selection. Through this approach more tangible model of the evaluation process may be presented. Proposed fuzzy method is indeed a multi criteria decision making model, based on the group of criteria, assigned for the evaluation procedure. A case study is also conducted, based on the opinion of an invented group of the experts.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.11
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• pp.63-71
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• 2010

FormFactors is one of the most critical design factors in early development of mobile phone, and proper selection of FormFactors is necessary for smoothness of product development and customer satisfaction. Especially, emergence of mobile convergence added so various functions besides its original call function that importance of selecting FormFactors has increased because multiform FormFactors are needed. However there is problem such as frequent change of decision making in existing process because established process picks out FormFactors sensibly and arbitrarily through idea pull and so forth. We proposed FormFactors selection process by Axiomatic Design, set approach method and block approach method for reasonable and systematical FormFactors selection. First of all, we set the purpose of mobile phone development, and it is examined by Axiomatic Design. FormFactors design matrix is deduced through this process, the numbers of axes and rails are proposed using set approach method, and then patterns of FormFactors are embodied by block approach method. Particularly process application was tried through case study of mobile phone development, and we ensured that new FormFactors can be presented to a designer by systematical verification if change of customer requirements occurs through our process.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.99-104
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• 2002

This paper addresses a problem of machining route selection and determination of input quantity with yield in multi-stage flexible flow system. The problem is formulated as nonlinear programing and the proposed model is solved by genetic algorithm(GA) approach. The effectiveness of the proposed GA approach is evaluated through comparisons with the optimal solution obtained from the branch and bound for the same problem.

• Journal of the Korean Statistical Society
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• v.25 no.1
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• pp.1-24
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• 1996

In selecting a final tree, Breiman, Friedman, Olshen, and Stone(1984) compare the prediction risks of a pair of tree, where one contains the other, using the standard error of the prediction risk of the larger one. This paper proposes an approach to selection of a final tree by using the standard error of the difference of the prediction risks between a pair of trees rather than the standard error of the larger one. This approach is compared with CART's for simulated data from a simple regression model. Asymptotic results of the approaches are also derived and compared to each other. Both the asymptotic and the simulation results indicate that final trees by CART tend to be smaller than desired.

• Korean Journal of Mathematics
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• v.22 no.2
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• pp.289-305
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• 2014

In this paper we propose a new approach to the variable selection problem for a primary resolution and wavelet basis functions in wavelet regression. Most wavelet shrinkage methods focus on thresholding the wavelet coefficients, given a primary resolution which is usually determined by the sample size. However, both a primary resolution and the basis functions are affected by the shape of an unknown function rather than the sample size. Unlike existing methods, our method does not depend on the sample size and also takes into account the shape of the unknown function.

• Journal of the Korean Statistical Society
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• v.31 no.1
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• pp.45-61
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• 2002

We consider the problem of estimating binomial proportions in the presence of nonignorable nonresponse using the Bayesian selection approach. Inference is sampling based and Markov chain Monte Carlo (MCMC) methods are used to perform the computations. We apply our method to study doctor visits data from the Korean National Family Income and Expenditure Survey (NFIES). The ignorable and nonignorable models are compared to Stasny's method (1991) by measuring the variability from the Metropolis-Hastings (MH) sampler. The results show that both models work very well.

• Journal of the Korean Statistical Society
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• v.20 no.2
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• pp.99-107
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• 1991

Behrens-Fisher problem is viewed from a model selection approach. Normal distribution is regarded as an approximating model, A criterion, called TIC, is derived and is compared with selection criteria such as AIC and a bootstrap estimator. Stochastic approximation is used since no closed form expression is available for the bootstrap estimator.

• Korean Management Science Review
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• v.17 no.2
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• pp.55-65
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• 2000

This paper addresses a problem of machining route selection in multi-stage process with machine group. This problem is considered the subcontracting and the production in-house such as regular and overtime work. the proposed model is formulated as a 0-1 integer programming constraining the avaliable time of each machine for planning period and total overtimes. The objective of the model is to minimize the sum of processing cost, overtime cost, and subcontracting cost. To solve this model, a genetic algorithm(GA) approach is developed. The effectiveness of the proposed GA approach is evaluated through comparisons with the optimal solution obtained from the branch and bound. In results, the same optimal solution is obtained from two methods at small size problem, and the consistent solution is provided by the GA approach at large size problem. The advantage of the GA approach is the flexibility into decision-making process because of providing multiple machining routes.