• 한국의류학회지
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• 제7권1호
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• pp.27-35
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• 1983

The purpose of this study is to find out the relationship between needs and factors of clothing Selection. 400 women students from 4 universities and colleges in Seoul were selected and tested. And Hwang Jeongkyu's Needs inventory test sheets for need inventory test and the questionnaire based on Lee Eunju's study for factors of clothing selection were given to the sample. The test data was computerized to get the relationship. The results are as follows: (I) Behavior of clothing selection according to expression of individuality correlated significantly with achievement, aggression, dominance, emotionality, exhibitionism, sex, and autonomy at the .01 level. (2) Behavior of clothing selection according to utility correlated significantly with abasement, and affiliation at the .01 level. and with emotionality at the .05 level, (3) Behavior of clothing selection according to economy correlated significantly with abasement, affiliation, dominance, emotionality, and exhibitionism at the .05 level, and with achievement at the .01 level. (4) Behavior of clothing selection according to modesty correlated significantly with abasement, exhibitionism, and sex at the .01 level. (5) Behavior of clothing selection according to conformity correlated significantly with abasement, emotionality, and autonomy at the .01 level, and with dominance at the .05 level.

• 대한가정학회지
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• 제23권4호
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• pp.1-7
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• 1985

The aim of this study is to find out the relation-ship between personality and te factors for clothing selection. For the test, 396 high school girl students in Seoul were selected, respectively. The general personality test sheets by Kim Giseok and the questionnaire besed on Park Eunju's study on the factors for clothing selection were given to the sample group. RESULTS : 1. Behavior of clothing selection according to expression of individuality is sign ificantly correlated with ascendancy and sociability at the level of .001, and with emotional stability at .50. 2. Behavior of clothing selection according to utility is significantly correlated with resopnsibility and emotional stability at the level of .01. 3. Behavior of clothing selection according to economy is significantly correlated with ascendancy and self-confidence at the level of .01, and with responsibility and sociablity at .05. 4. Behavior of clothing selection according to modesty is significantly correlated with ascendancy and sociability at level of .001, and with self-confidence at .01. 5. Behavior of clothing selection according to conformity is significantly correlated with ascendancy, emothional stability, and self-confidence at the level of .001, and with sociability at .05.

• 농촌계획
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• 제27권3호
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• pp.47-56
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• 2021

The purpose of this study is to identify the determinants of local farmers' participation and acceptance level in traditional agricultural technology conservation activities, using on the traditional agricultural water utilization system in Uiseong designated as Korea's important agricultural heritage system No. 10. The Heckman sample selection model was used to solve the selection bias. The results show that as the interest in the conservation of the agricultural ecological and environment is high, as the traditional agricultural system in Uiseong is used, as the cultivation area of paddy field increased, and as the age 50s or older, the willingness to participate in conservation activities increased. The results also indicate that as the experience of participating in the conservation of the agricultural ecological and environment and the hours of education are increased, the knowledge of the traditional agricultural system in Uiseong is low, and as the cultivation area of paddy field decreased, the acceptance time for conservation activities increased. The results of this study may contribute to government's policy for traditional agricultural technology conservation.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2013년도 제47차 동계학술대회논문집 21권1호
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• pp.197-199
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• 2013

신경망의 학습은 학습 샘플의 품질뿐만이 아니라 입력으로 사용되는 특징에도 영향을 받으므로 신경망의 출력을 결정하는데 있어 연관성이 높은 특징을 입력으로 사용함으로써 학습된 신경망의 전체적인 성능을 높일 수 있다. 이 논문에서는 신경망의 입력으로 사용되는 특징과 출력의 연관성 파악하고 연관성이 낮은 특징을 학습 과정에서 배제함으로써 신경망의 전체적인 성능을 높일 수 있는 방법을 제시하였다. 토석류 데이터를 위한 의사 샘플 신경망에 제안한 방법을 적용한 경우 연관성이 낮은 특징 하나를 제외함으로써 약 6%의 오류 감소 효과를 얻을 수 있었다.

• 한국주거학회논문집
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• 제23권1호
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• pp.55-66
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• 2012

Present study investigates on the impact of resources and characteristics of the tenure choice for divorced women in Korea. The authors utilize the micro data from the Korea Census (2% sample) provided by the National Statistical Office. The authors apply the bivariate probit model to eliminate selection bias that could incur due to sample selectivity, from a chain of marital disruption and tenure choices. This study starts with a descriptive explanation of homeownership after divorce from 1985 to 2005. It concluded that divorce results in a substantial attrition of homeownership. The authors found that out for many women, divorce initiates a process of downward mobility on the housing ladder. The probability to own housing is much lower for divorced women than for women who are not divorced. The present study concludes by suggesting some policy implications for divorced women who have limited access to housing stability. The authors also suggest some future studies that can compensate the empirical limitations of the present study.

• Journal of the Korean Statistical Society
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• 제21권2호
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• pp.93-110
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• 1992

A model selection approach is used to find out whether the mean and the variance of a unique sample are different from the pre-specified values. Normal distribution is selected as an approximating model. Kullback-Leibler discrepancy comes out as a natural measure of discrepancy between the operating model and the approximating model. Several estimates of selection criterion are computed including AIC, TIC, and a coupleof bootstrap estimator of the selection criterion are considered according to the way of resampling. It is shown that a closed form expression is available for the parametric bootstrap estimated cirterion. A Monte Carlo study is provided to give a formal comparison when the operating family itself is normally distributed.

• Communications for Statistical Applications and Methods
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• 제20권4호
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• pp.259-270
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• 2013

This paper considers a penalized composite quantile regression (CQR) that performs a variable selection in the linear model with grouped variables. An adaptive sup-norm penalized CQR (ASCQR) is proposed to select variables in a grouped manner; in addition, the consistency and oracle property of the resulting estimator are also derived under some regularity conditions. To improve the efficiency of estimation and variable selection, this paper suggests the two-stage penalized CQR (TSCQR), which uses the ASCQR to select relevant groups in the first stage and the adaptive lasso penalized CQR to select important variables in the second stage. Simulation studies are conducted to illustrate the finite sample performance of the proposed methods.

• 가족자원경영과 정책
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• 제9권2호
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• pp.1-21
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• 2005

The purpose of this study was to analyze the effect of the influencing factors on the economic and educational supports selection of small self-employed business. A sample of 321 was selected from self-employed workers living in Daegu. For data analysis, logistic regression was used. The major findings were as follows: 1 The determinants of the economic supports selection in self-employed business were father's working experiences in self-employed business, taking employees or not in business, having housing ownership or not, and as well as the amount of starting capital. 2. The determinants of the educational supports selection in self-employed business were self-employer's working experience as self-employed before or not, and empowerment with self-employed work.

• Communications for Statistical Applications and Methods
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• 제26권3호
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• pp.273-293
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• 2019

We consider the problem of model selection in multiple linear regression with outliers and non-normal error distributions. In this article, the robust model selection criterion is proposed based on the robust estimation method with the least absolute deviation (LAD). The proposed criterion is shown to be consistent. We suggest proposed criterion based algorithms that are suitable for a large number of predictors in the model. These algorithms select only relevant predictor variables with probability one for large sample sizes. An exhaustive simulation study shows that the criterion performs well. However, the proposed criterion is applied to a real data set to examine its applicability. The simulation results show the proficiency of algorithms in the presence of outliers, non-normal distribution, and multicollinearity.

• Communications for Statistical Applications and Methods
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• 제31권3호
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• pp.337-347
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• 2024

Kernel density estimation is a prevalent technique employed for nonparametric density estimation, enabling direct estimation from the data itself. This estimation involves two crucial elements: selection of the kernel function and the determination of the appropriate bandwidth. The selection of the bandwidth plays an important role in kernel density estimation, which has been developed over the past decade. A range of methods is available for selecting the bandwidth, including the plug-in bandwidth. In this article, the proposed plug-in bandwidth is introduced, which leverages shifted Chebyshev series-based approximation to determine the optimal bandwidth. Through a simulation study, the performance of the suggested bandwidth is analyzed to reveal its favorable performance across a wide range of distributions and sample sizes compared to alternative bandwidths. The proposed bandwidth is also applied for kernel density estimation on real dataset. The outcomes obtained from the proposed bandwidth indicate a favorable selection. Hence, this article serves as motivation to explore additional plug-in bandwidths that rely on function approximations utilizing alternative series expansions.