• Journal of the Korean Statistical Society
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• 제24권2호
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• pp.349-360
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• 1995

This paper deals with the robustness properties of the minimum disparity estimation in linear regression models. The estimators defined as statistical quantities whcih minimize the blended weight Hellinger distance between a weighted kernel density estimator of the residuals and a smoothed model density of the residuals. It is shown that if the weights of the density estimator are appropriately chosen, the estimates of the regression parameters are robust.

• Communications for Statistical Applications and Methods
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• 제1권1호
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• pp.33-40
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• 1994

The conditional expectation of a random variable in a multivariate normal random vector is a multiple linear regression on its predecessors. Using this fact, the least median of squares estimation method developed in a multiple linear regression is adapted to a multivariate data to identify influential observations. The resulting method clearly detect outliers and it avoids the masking effect.

• Journal of the Korean Statistical Society
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• 제28권2호
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• pp.265-277
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• 1999

Classical and inverse estimation methods are two well known methods in statistical calibration problems. When there are outliers, both methods have large MSE's and could not estimate the input value correctly. We suggest median calibration estimation based on the LD-statistics. To investigate the robust performances, the influence function of the median calibration estimator is calculated and compared with other methods. When there are outliers in the response variables, the influence function is found to be bounded. In simulation studies, the MSE's for each calibration methods are compared. The estimated inputs as well as the performance of the influence functions are calculated.

• 응용통계연구
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• 제24권5호
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• pp.871-881
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• 2011

In this paper we propose a logistic regression method to estimate the survival function and the median survival time in interval-censored data. The proposed method is motivated by the data augmentation technique with no sacrifice in augmenting data. In addition, we develop a cross validation criterion to determine the size of data augmentation. We compare the proposed estimator with other existing methods such as the parametric method, the single point imputation method, and the nonparametric maximum likelihood estimator through extensive numerical studies to show that the proposed estimator performs better than others in the sense of the mean squared error. An illustrative example based on a real data set is given.

• Journal of the Korean Data and Information Science Society
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• 제21권6호
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• pp.1319-1325
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• 2010

Quantile regression provides a more complete statistical analysis of the stochastic relationships among random variables. Sometimes quantile functions estimated at different orders can cross each other. We propose a new non-crossing quantile regression method applying support vector median regression to restricted regression quantile, restricted support vector quantile regression. The proposed method provides a satisfying solution to estimating non-crossing quantile functions when multiple quantiles for high dimensional data are needed. We also present the model selection method that employs cross validation techniques for choosing the parameters which aect the performance of the proposed method. One real example and a simulated example are provided to show the usefulness of the proposed method.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권7호
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• pp.344-351
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• 2006

The fatigue of back muscle in the repetitive lifting motion was studied using multiple parameters(FFT_MDF, RMS, 2C, NT) in this study. Recent developments in the time-frequency analysis procedures to compute the IMDF(instantaneous median frequency) were utilized to overcome the nonstationarity of EMG signal using Cohen-Posch distribution. But the above method has a lot of computation time because of its complexity. So, in this study, FFT_MDF(median frequency estimation based on FFT) algorithm was used for median frequency estimation of back muscle EMG signal during muscle work in uniform velocity portion of lumbar movement. The analysis period of EMG signal was determined by using the run test and lumbar movement angle in dynamic task, such as lifting. Results showed that FFT_MDF algorithm is well suited for the estimation of back muscle fatigue from the view point of computation time. The negative slope of a regression line fitted to the median frequency values of back muscle EMG signal was taken as an indication of muscle fatigue. The slope of muscle fatigueness with FFT_MDF method shows the similarity of 77.8% comparing with CP_MDF(median frequency estimation based on Cohen Posch distribution) method.

• 가족자원경영과 정책
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• 제21권4호
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• pp.1-15
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• 2017

The purpose of this study was to identify the effect of satisfaction with the marriage support policy on marriage intention among unmarried employed men and women. The study subjects included 300 unmarried working men and women aged over 30 years residing in the Seoul metropolitan area. First, according to the analysis of the subjects' overall tendency towards satisfaction with the marriage support policy and towards marriage intention, the marriage support policy gained the highest level of support in relation to housing for newlywed couples, which scored 3.29 (sd=.93), followed by improvements in corporate and family culture for the purposes of work-family compatibility at 3.24 (sd=.95), wedding loans at 3.18 (sd=1.01), and the paid leave system for marriage preparation at 3.12 (sd=.88). These variables scored slightly higher than the median 3 points. Conversely, satisfaction with the availability of marriage-related information and the provision of opportunities for dating scored 2.65 (sd=.88) and 2.78 (sd=.80), respectively, both of which were slightly lower than the median of 3. The overall mean score for satisfaction with the marriage support policy was 3.03 (sd=.95), which was slightly higher than the median of 3. In regards to marriage intention, the score was 3.32 (sd=1.15) points out of a perfect score of 5, which was slightly higher than the median. This indicated a slightly higher level in terms of the subjects' intention to marry. Second, a hierarchical regression analysis was performed to identify the effect of satisfaction with the marriage support policy on marriage intention among unmarried employed adults. Sociodemographic variables were entered as control variables in the regression at the first stage, and variables relating to satisfaction in a family-friendly social environment were entered at the second stage. When gender, age, educational level, monthly income, period of employment, working hours, and type of employment were inserted in the first stage of regression as control variables, gender, monthly income, period of employment, and type of employment were found to have a significant effect on marriage intention. Marriage intention was found to be greater in unmarried men with higher monthly incomes and longer periods spent working, and in unmarried working men and women engaged in tenured employment work. When variables relating to satisfaction with the marriage support policy were inserted in the second stage of regression, gender, monthly income, type of employment, and satisfaction with direct marriage support had significant effects on marriage intention. It was found that marriage intention was greater in unmarried men whose monthly income was higher, whose employment type was tenured work, and who showed greater satisfaction with direct marriage support.

• Communications for Statistical Applications and Methods
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• 제13권2호
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• pp.233-241
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• 2006

Classical principal component analysis (PCA) can be formulated as finding the linear subspace that best accommodates multidimensional data points in the sense that the sum of squared residual distances is minimized. As alternatives to such LS (least squares) fitting approach, we produce LMS (least median of squares) and LTS (least trimmed squares)-type PCA by minimizing the median of squared residual distances and the trimmed sum of squares, in a similar fashion to Rousseeuw (1984)'s alternative approaches to LS linear regression. Proposed methods adopt the data-driven optimization algorithm of Croux and Ruiz-Gazen (1996, 2005) that is conceptually simple and computationally practical. Numerical examples are given.

• 대한가정학회지
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• 제40권12호
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• pp.171-187
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• 2002

The purpose of this study ws6 to find out women's informationized conditions and their life changes. The subjects of this research were 482 women living in large and small-medium sized cities. The data analysis used average, pearson's correlation, one-way ANOVA and multiple regression by SAS/PC. The major findings were as follows: 1. Women's level of informationized conditions is under median in computer using and information technology. The effect of informatization is over median about improving the ability, recognizing from family and neighbor, achieving the occupation. The change of individual and family is under median except educational stimulus, life vitality and educational effect on the family. 2. The effect of informatization was influenced by ability of using computer, necessity and purpose of informatization. The change of individual was influenced by ability of using computer, purpose and effect of informatization and also the change of family was influenced by handicapped reason in information education and the effect of informatization.

• Communications for Statistical Applications and Methods
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• 제21권4호
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• pp.285-296
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• 2014

The quantile residual life function has been effectively used to interpret results from the analysis of the proportional hazards model for censored survival data; however, the quantile residual life function is not always estimable with currently available semi-parametric regression methods in the presence of heavy censoring. A parametric regression approach may circumvent the difficulty of heavy censoring, but parametric assumptions on a baseline hazard function can cause a potential bias. This article proposes a Bayesian semi-parametric regression approach for inference on an unknown baseline hazard function while adjusting for available covariates. We consider a model-based approach but the proposed method does not suffer from strong parametric assumptions, enjoying a closed-form specification of the parametric regression approach without sacrificing the flexibility of the semi-parametric regression approach. The proposed method is applied to simulated data and heavily censored survival data to estimate various quantile residual lifetimes and adjust for important prognostic factors.