• Journal of the Korean Data and Information Science Society
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• 제22권5호
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• pp.951-959
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• 2011

The proposed method is based on a penalized censored regression model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log likelihood function of censored regression model. It provide the efficient computation of regression parameters including variable selection and leads to the generalized cross validation function for the model selection. Numerical results are then presented to indicate the performance of the proposed method.

• 산업경영시스템학회지
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• 제23권58호
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• pp.17-27
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• 2000

In FMS where tool movement policy is adopted, a mathematical model has been developed which determines the selection of a tool type for each operation and tool loading-part assignment simultaneouly. The objective is to minimize the total cost of operation including machining time cost, tool cost, tool replacement and loading time cost, and tool change time cost. Due to the complexity of the problem, an approximate solution procedure has been developed utilizing the special structure of the model. Tool selection was determined first to allocate one tool type to each operation considering more than one tool type alternatives for each operation. Tool loading-part assignment was determined to minimize tile total number of tool changes due to part mix based on the tool selection.

• Communications for Statistical Applications and Methods
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• 제22권2호
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• pp.173-180
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• 2015

We propose a selection procedure of principal components in principal component regression. Our method selects principal components using variable selection procedures instead of a small subset of major principal components in principal component regression. Our procedure consists of two steps to improve estimation and prediction. First, we reduce the number of principal components using the conventional principal component regression to yield the set of candidate principal components and then select principal components among the candidate set using sparse regression techniques. The performance of our proposals is demonstrated numerically and compared with the typical dimension reduction approaches (including principal component regression and partial least square regression) using synthetic and real datasets.

• Communications for Statistical Applications and Methods
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• 제24권6호
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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.

• 품질경영학회지
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• 제26권1호
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• pp.143-160
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• 1998

This article is concerned with the selection of subsets of predictor variables to be included in bulding the binary response logistic regression model. It is based on a Bayesian aproach, intended to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets. This procedure reformulates the logistic regression setup in a hierarchical normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. It is done by use of the fact that cdf of logistic distribution is a, pp.oximately equivalent to that of $t_{(8)}$/.634 distribution. The a, pp.opriate posterior probability of each subset of predictor variables is obtained by 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 that with highest posterior probability. To highlight the merit of this procedure a couple of illustrative numerical examples are given.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.323-330
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• 2009

This paper presents the selection, examination, and user test (SEUT) procedure to determine the ideal color scheme for a 4D model. This systematic procedure can be performed iteratively to obtain the color scheme that would be most appropriate for construction purposes. To verify the proposed procedure, an example case with two iterations is presented. Ten color schemes were examined and 48 users tested during the two iterations, and the result shows that the SEUT procedure is an effective method for determining the ideal color scheme for 4D models.

• 품질경영학회지
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• 제13권2호
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• pp.29-36
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• 1985

The problem of selecting the gamma population with the largest mean out of k gamma populations, each of which has the same shape parameter is considered. An elimination type two-stage procedure is proposed which guarantees the same probability requirement using the indifference-zone approach as does the single-stage procedure of Gibbons, Olkin and Sobel (1977). The two-stage procedure has the highly desirable property that the expected total number of observations required by the procedure is always less than that of the corresponding single-stage procedure regardless of the configuration of the population parameters.

• 대한산업공학회지
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• 제20권4호
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• pp.99-111
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• 1994

Toguchi method is a systematic technique for designing high quality product at low cost. There are three steps in the Toguchi method, 1)system design, 2)parameter design, and 3)tolerance design. This paper considers the tolerance design for multiple performance characteristics which is practically important. We present two tolerance design procedures : grade selection and tolerance determining procedures. In grade selection procedure a scheme is presented that minimizes the sum of the price of low-level characteristics and the expected loss due to the variations of high-level characteristics. In tolerance determining procedure we determine the tolerances of the low-level characteristics.

• Journal of the Korean Statistical Society
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• 제16권1호
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• pp.26-36
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• 1987

In this paper, we study on a subset selection procedure based on Hodges-Lehmann estimators derived from the Wilcoxon test. To estimate the standard error of the Hodges-Lehmann estimators, the biweight A-estimator of scale is used. The Pitman efficiency of the proposed rule is compared with the Gupta's rule and the trimmed-means rule through a small-sample Monte Carlo study. The results show that the proposed rule satisfies the $P^*$-condition and is very efficient in various heavy-tailed distributions.

• Communications for Statistical Applications and Methods
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• 제16권1호
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• pp.143-155
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• 2009

Best subset selection procedure based on mutual information (MI) between a set of explanatory variables and a dependent class variable is suggested. Derivation of multivariate MI is based on normal mixtures. Several types of normal mixtures are proposed. Also a best subset selection algorithm is proposed. Four real data sets are employed to demonstrate the efficiency of the proposals.