• Journal of the Korean Statistical Society
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• v.26 no.2
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• pp.245-259
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• 1997

The paper shows that certain hierachical Bayes (HB) predictors for small domain data in repeated surveys "universally" or "stochastically" dominate all linear unbiased predictors. Also, the HB predictors are "best" within the class of all equivariant predictors under a certain group of transformations.tain group of transformations.

• Journal of Korean Medicine for Obesity Research
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• v.18 no.2
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• pp.115-127
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• 2018

Objectives: People often fail to reduce or maintain their weight despite trying to lose weight. The purpose of this study was to review previously published study results of the predictive factors associated with weight loss in obesity treatment. Methods: Authors searched for the articles related to weight loss, published from 2007 to 2017 found on PubMed, Scopus, Research Information Sharing Service (RISS), and Koreanstudies Information Service System (KISS). A total of 43 articles were finally selected. From the study results, unchangeable and changeable predictors were extracted, and these predictors were examined according to detailed categories. Results: Predictors of weight loss in obesity treatment included genetic and physiological factors, demographic factors, history of treatment on obesity related factors, behavioral factors, psychological factors and treatment process related factors. The main factors of weight loss were unchangeable predictors such as high initial degree of obesity and younger age, and changeable predictors such as dietary restraint, regular exercise, self-efficacy, initial weight loss and attendance. Especially dietary restraint, regular exercise, successful initial weight loss and high attendance were considered to be dominant factors for weight loss treatments. Conclusions: Our review results suggest that unchangeable and changeable predictors of weight loss should be carefully examined during treatments of obesity.

• Journal of the Korean Society of Clothing and Textiles
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• v.12 no.3 s.28
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• pp.295-307
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• 1988

The purposes of the study were to identify the general predictors of fashion leadership and to compare the fashion leadership predictors among different social groups. Thirty-one variables (thirteen fashion-related variables, five social variables, nine psychological variables, four demographic variables) were included in the regression analysis. Data were obtained from 446 women living in Seoul area by self-administered questionnaire. The results of the study were as follows: 1. Seven variables explained about 64 percent of the total variance of fashion leadership. The most important predictors of fashion leadership were fashion interest, use of marketer-dominated fashion information source, and 'stable-creative' self-image. 2. The predictors that consistently predict fashion leadership across different social groups (students, career women, housewives) were fashion interest and use of marketer-dominated information source. The predictors of innovativeness and opinion leadership were very different among groups.

• The Journal of Korean Medicine
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• v.37 no.3
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• pp.62-73
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• 2016

Objectives: Dropout and weight regain are common problems in most obesity treatments. The purpose of this study was to review previously published study results of the predictive factors associated with dropout during weight loss treatment and weight loss maintenance after successful weight loss. Methods: Authors searched for the articles related to dropout and weight loss maintenance, published from 2007 to 2016 found on Pubmed, Scopus, RISS, and KISS. A total of 19 articles were finally selected. From the study results, unchangeable and changeable predictors were extracted, and these predictors were examined according to dropout and weight loss maintenance categories. Results: The unchangeable predictors of dropout were younger age, lower education level and female, whereas the changeable predictors of dropout were lower initial weight loss, symptoms of depression and body dissatisfaction. The strongest factor for predicting the dropout was initial weight loss. The unchangeable predictors of weight loss maintenance were old age, male and family history of obesity, whereas the changeable predictors of weight loss maintenance were regular exercise, dietary restraint, self-weighing and low depressive symptoms. Initial weight loss, depressive symptoms, body image, dietary restraint, physical activity, weight loss expectation and social support were considered to be dominant factors for weight loss treatments. Conclusions: Our review results suggest that unchangeable and changeable predictors of dropout and weight loss maintenance should be carefully examined during treatments of obesity.

• Journal of Korean Medicine for Obesity Research
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• v.19 no.2
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• pp.119-136
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• 2019

Objectives: People often fail to maintain their weight even though they have succeeded in weight loss. The purpose of this study was to review previously published study results with regards to the predictive factors associated with weight loss maintenance after successful weight loss. Methods: The authors searched for the articles related to weight loss maintenance after successful weight loss, published up until June 2019 on PubMed, Cochrane Central Register of Controlled Trials (CENTRAL), Embase, Research Information Sharing Service (RISS), and Koreanstudies Information Service System (KISS). A total of 76 articles were finally selected. From the study results, changeable and unchangeable predictors were extracted, and these predictors were examined according to detailed categories. Results: The changeable predictors of weight loss maintenance included behavioral factors, psychological factors and treatment process-related factors, whereas the unchangeable predictors included genetic and physiological factors, demographic factors, history of treatment on obesity-related factors. The main factors of weight loss maintenance were changeable predictors such as healthy eating habits, dietary intake control, binge eating control, regular exercise and physical activity, depression and stress control, social supports, self-regulation, self-weighing and initial weight loss and unchangeable predictors such as low initial weight and maximum lifetime weight. Conclusions: The results of our review results suggest that changeable and unchangeable predictors of weight loss maintenance should be carefully examined during treatments of obesity.

• Journal of Intelligence and Information Systems
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• v.13 no.2
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• pp.15-26
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• 2007

In this study, a SOHO (Small Office Home Office) bankruptcy prediction model is proposed using Modified Bagging Predictors which is modification of traditional Bagging Predictors. There have been several studies on bankruptcy prediction for large and middle size companies. However, little studies have been done for SOHOs. In commercial banks, loan approval processes for SOHOs are usually less structured than those for large and middle size companies, and largely depend on partial information such as credit scores. In this study, we use a real SOHO loan approval data set of a Korean bank. First, decision tree induction techniques and artificial neural networks are applied to the data set, and the results are not satisfactory. Bagging Predictors which has been not previously applied for bankruptcy prediction and Modified Bagging Predictors which is proposed in this paper are applied to the data set. The experimental results show that Modified Bagging Predictors provides better performance than decision tree inductions techniques, artificial neural networks, and Bagging Predictors.

• Journal of Environmental Science International
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• v.22 no.8
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• pp.953-963
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. Forecasting of seasonal inflow to Andong dam is performed and assessed using statistical methods based on hydrometeorological data. Predictors which is used to forecast seasonal inflow to Andong dam are selected from southern oscillation index, sea surface temperature, and 500 hPa geopotential height data in northern hemisphere. Predictors are selected by the following procedure. Primary predictors sets are obtained, and then final predictors are determined from the sets. The primary predictor sets for each season are identified using cross correlation and mutual information. The final predictors are identified using partial cross correlation and partial mutual information. In each season, there are three selected predictors. The values are determined using bootstrapping technique considering a specific significance level for predictor selection. Seasonal inflow forecasting is performed by multiple linear regression analysis using the selected predictors for each season, and the results of forecast using cross validation are assessed. Multiple linear regression analysis is performed using SAS. The results of multiple linear regression analysis are assessed by mean squared error and mean absolute error. And contingency table is established and assessed by Heidke skill score. The assessment reveals that the forecasts by multiple linear regression analysis are better than the reference forecasts.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.977-985
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• 2010

In this paper, we propose an approach to conduct partial sufficient dimension reduction with heavily unbalanced categorical predictors. For this, we consider integrated categorical predictors and investigate certain conditions that the integrated categorical predictor is fully informative to partial sufficient dimension reduction. For illustration, the proposed approach is implemented on optimal partial sliced inverse regression in simulation and data analysis.

• The KIPS Transactions:PartA
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• v.9A no.4
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• pp.497-502
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• 2002

Conditional branches can severely limit the performance of instruction level parallelism by causing branch penalties. 2-level adaptive branch predictors were developed to get accurate branch prediction in high performance superscalar processors. Although 2 level adaptive branch predictors achieve very high prediction accuracy, they tend to be very costly. In this paper, set-associative cached correlated 2-level branch predictors are proposed to overcome the cost problem in conventional 2-level adaptive branch predictors. According to simulation results, cached correlated predictors deliver higher prediction accuracy than conventional predictors at a significantly lower cost. The best misprediction rates of global and local cached correlated predictors using set-associative caches are 5.99% and 6.28% respectively. They achieve 54% and 17% improvements over those of the conventional 2-level adaptive branch predictors.

• Journal of Korean Academy of Nursing
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• v.37 no.4
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• pp.459-466
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• 2007

Purpose: This study was to identify predictors of quality of life in breast cancer patients. Physical and pscyhological factors like stress, mood, and fatigue with sociodemographic factors like education, income, job and stage of disease were used to predict quality of life. Methods: One hundred eleven patients with breast cancer participated in this study? The functional Assessment of Cancer Therapy-Breast(FACT-B) was used to assess quality of life. Results: The mean age of the patients was 46.7 years. The FACT-B mean score was 89.89(SD:17.31) Education, income, job and stage of disease were significantly associated with QOL. In a regression analysis, mood, income, and fatigue were significant predictors for QOL where as, stress was not significant. Among the subscales of QOL, physical well-being, functional well-being, emotional well-being, and the breast cancer subscale were included as predictors of QOL Conclusion: Physical and psychological factors were strong predictors of QOL. These results demonstrate the need for interventions to improve QOL in breast cancer survivors.