• Journal of the Korean Data and Information Science Society
• /
• v.24 no.6
• /
• pp.1543-1550
• /
• 2013

In this paper, we study Bayesian estimation for the finite population proportion in binary data under selection bias. We use a Bayesian nonignorable selection model to accommodate the selection mechanism. We compare four possible estimators of the finite population proportions based on data analysis as well as Monte Carlo simulation. It turns out that nonignorable selection model might be useful for weekly biased samples.

• Proceedings of the Korean Statistical Society Conference
• /
• 2001.11a
• /
• pp.89-94
• /
• 2001

In this study we focus on variable selection in decision tree growing structure. Some of the splitting rules and variable selection algorithms are discussed. We propose a competitive variable selection method based on Kruskal-Wallis test, which is a nonparametric version of ANOVA F-test. Through a Monte Carlo study we note that CART has serious bias in variable selection towards categorical variables having many values, and also QUEST using F-test is not so powerful to select informative variables under heavy tailed distributions.

• Progress in Superconductivity and Cryogenics
• /
• v.23 no.3
• /
• pp.26-31
• /
• 2021

Advanced classification of Cs contaminated soil by using a magnetic force-assisted selection pipe was investigated. A selection pipe is a device that sort particles depending on their particle size, based on the relationship between buoyancy, drag, and gravity force acting on the particles. Radioactive cesium is concentrated in small-particle size soil components with a large specific surface area. Hence, the volume of the Cs contaminated soil can be reduced by recycling the large-particle size soil components with low radioactive concentration. One of the problems of the selection pipe was that the radioactive concentration of the stayed soil in the selection pipe exceeds 8000 Bq/kg, which is the standard value of recycling of Cs contaminated soil, due to low classification accuracy. In this study, magnetic fields were applied to the lab-scale selection pipe from upper side to improve the classification accuracy and to reduce the radioactive concentration of the stayed soil.

• International Journal of Control, Automation, and Systems
• /
• v.1 no.4
• /
• pp.453-458
• /
• 2003

The anomaly-detection algorithm based on negative selection of T cells is representative model among self-recognition methods and it has been applied to computer immune systems in recent years. In immune systems, T cells are produced through both positive and negative selection. Positive selection is the process used to determine a MHC receptor that recognizes self-molecules. Negative selection is the process used to determine an antigen receptor that recognizes antigen, or the nonself cell. In this paper, we propose a novel self-recognition algorithm based on the positive selection of T cells. We indicate the effectiveness of the proposed algorithm by change-detection simulation of some infected data obtained from cell changes and string changes in the self-file. We also compare the self-recognition algorithm based on positive selection with the anomaly-detection algorithm.

• Journal of the East Asian Society of Dietary Life
• /
• v.22 no.6
• /
• pp.895-902
• /
• 2012

This study was conducted to understand the Ramyun's selection attributes of Korean adults and examine differences in demographic characteristics and meal intake behavior among three groups of samples divided based on the Ramyun's selection attributes. Self-administered questionnaires were completed by 702 adults, and data were subjected to frequency analysis, chi-square analysis, factor analysis, reliability tests, cluster analysis, and discriminant analysis using SPSS. The results of the study were as follows. The Ramyun's selection attributes for Korean adults investigated were food quality (four variables), price (three variables), and company reliability (four variables). Cluster analysis resulted in the subjects being divided into three groups according to their Ramyun's selection attributes, a high-selection group, mid-selection group, and low-selection group. Three groups of samples classified by Ramyun's selection attributes differed based on demographic characteristics (gender and education level) and meal intake behavior (meal numbers, reason for meal, meal time, and meal size).

• The Journal of Asian Finance, Economics and Business
• /
• v.6 no.2
• /
• pp.213-221
• /
• 2019

The study aims to investigate some criteria of supplier selection in the textile and apparel (T&A) sector in Vietnam. Most research on supplier selection criteria for T&A sector was mainly conducted based on the review of literature. Therefore, the purpose of this study is to explore these criteria based on a framework in which an integrated approach of qualitative and quantitative was employed. First, an in-depth interview was used to explore what supplier selection criteria T&A companies were utilized after the literature on supplier selection criteria had been reviewed. Next, a prequestionnaire was built and sent to some practitioners and experts for their revision. Then, a pilot survey of 31 T&A companies with numerous statistical tests was conducted to validate the questionnaire. Finally, an official study of 282 respondents was conducted to determine supplier selection criteria which are best suited for T&A companies through exploratory factor analysis. The findings of the study suggest that there are eight supplier selection criteria including Quality, Cost, Delivery, Service, Capability, Company's image, Relationship, and Sourcing country. Each criterion comprises certain sub-criteria to make the supplier selection criteria set more comprehensive. The findings will be a contribution to the selection process of T&A companies as they can utilize these criteria to select capable suppliers.

• Asian-Australasian Journal of Animal Sciences
• /
• v.31 no.1
• /
• pp.19-25
• /
• 2018

Objective: In practical breeding, selection is often performed by ignoring the accuracy of evaluations and applying economic weights directly to the selection index coefficients of genetically standardized traits. The denominator of the standardized component trait of estimated genetic evaluations in practical selection varies with its reliability. Whereas theoretical methods for calculating the selection index coefficients of genetically standardized traits account for this variation, practical selection ignores reliability and assumes that it is equal to unity for each trait. The purpose of this study was to clarify the effects of ignoring the accuracy of the standardized component trait in selection criteria on selection responses and economic weights in retrospect. Methods: Theoretical methods were presented accounting for reliability of estimated genetic evaluations for the selection index composed of genetically standardized traits. Results: Selection responses and economic weights in retrospect resulting from practical selection were greater than those resulting from theoretical selection accounting for reliability when the accuracy of the estimated breeding value (EBV) or genomically enhanced breeding value (GEBV) was lower than those of the other traits in the index, but the opposite occurred when the accuracy of the EBV or GEBV was greater than those of the other traits. This trend was more conspicuous for traits with low economic weights than for those with high weights. Conclusion: Failure of the practical index to account for reliability yielded economic weights in retrospect that differed from those obtained with the theoretical index. Our results indicated that practical indices that ignore reliability delay genetic improvement. Therefore, selection practices need to account for reliability, especially when the reliabilities of the traits included in the index vary widely.

• The Research Journal of the Costume Culture
• /
• v.23 no.2
• /
• pp.213-227
• /
• 2015

This study understood what effect was produced on the purchasing decision making of outdoor wear by a shopping orientation, fashion involvement and demographic characteristics offered practical suggestions as to what effect was produced on the store selection criteria, product selection criteria for purchasing decision making in purchasing outdoor wear. This research was conducted through a questionnaire survey, and 397 males in were collected for analysis. The results were as follows. First, shopping orientation group was classified into hedonic shopping orientation group and utilitarian shopping orientation group. And it was classified into high fashion involvement group and low fashion involvement group according to fashion involvement. Product selection criteria were classified into 2 factors such as intrinsic attributes and extrinsic attributes. And store selection criteria were classified into 4 factors such as store atmosphere, store environment, promotion and salesmen. Second, there was partly significant difference in product selection criteria, and store selection criteria between utilitarian shopping group and hedonic shopping group. Third, there was significant difference in product selection criteria and store selection criteria between high fashion involvement group and low fashion involvement group. Finally, there was significant difference in the and according to age, job, and income among demographic characteristics.

• Asian-Australasian Journal of Animal Sciences
• /
• v.33 no.12
• /
• pp.1912-1921
• /
• 2020

Objective: This study assessed genomic prediction accuracies based on different selection methods, evaluation procedures, training population (TP) sizes, heritability (h²) levels, marker densities and pedigree error (PE) rates in a simulated Korean beef cattle population. Methods: A simulation was performed using two different selection methods, phenotypic and estimated breeding value (EBV), with an h² of 0.1, 0.3, or 0.5 and marker densities of 10, 50, or 777K. A total of 275 males and 2,475 females were randomly selected from the last generation to simulate ten recent generations. The simulation of the PE dataset was modified using only the EBV method of selection with a marker density of 50K and a heritability of 0.3. The proportions of errors substituted were 10%, 20%, 30%, and 40%, respectively. Genetic evaluations were performed using genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) with different weighted values. The accuracies of the predictions were determined. Results: Compared with phenotypic selection, the results revealed that the prediction accuracies obtained using GBLUP and ssGBLUP increased across heritability levels and TP sizes during EBV selection. However, an increase in the marker density did not yield higher accuracy in either method except when the h² was 0.3 under the EBV selection method. Based on EBV selection with a heritability of 0.1 and a marker density of 10K, GBLUP and ssGBLUP_0.95 prediction accuracy was higher than that obtained by phenotypic selection. The prediction accuracies from ssGBLUP_0.95 outperformed those from the GBLUP method across all scenarios. When errors were introduced into the pedigree dataset, the prediction accuracies were only minimally influenced across all scenarios. Conclusion: Our study suggests that the use of ssGBLUP_0.95, EBV selection, and low marker density could help improve genetic gains in beef cattle.

• Communications for Statistical Applications and Methods
• /
• v.26 no.2
• /
• pp.149-161
• /
• 2019

In this article, we suggest the following approaches to simultaneous variable selection and outlier detection. First, we determine possible candidates for outliers using properties of an intercept estimator in a difference-based regression model, and the information of outliers is reflected in the multiple regression model adding mean shift parameters. Second, we select the best model from the model including the outlier candidates as predictors using stochastic search variable selection. Finally, we evaluate our method using simulations and real data analysis to yield promising results. In addition, we need to develop our method to make robust estimates. We will also to the nonparametric regression model for simultaneous outlier detection and variable selection.