• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.325-335
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• 2002

In this paper we introduce some adaptive M-estimators using selector statistics to estimate the center of symmetric and continuous underlying distributions. This selector statistics is based on the idea of Hogg(1983) and Hogg et. al. (1988) who used averages of some order statistics to discriminate underlying distributions. In this paper, we use the functions of sample quantiles as selector statistics and determine the suitable quantile points based on maximizing the distance index to discriminate distributions under consideration. In Monte Carlo study, this robust estimation method works pretty good in wide range of underlying distributions.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.173-179
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• 2000

Interval prediction based on the empirical distribution function for the class of time series with time varying coefficients is discussed. To this end, strong mixing property of the model is shown and results due to Fotopoulos et. al.(1994) are employed. A simulation study is presented to assess the accuracy of the proposed interval predictor.

• Journal of the Korea Society for Simulation
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• v.9 no.3
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• pp.43-51
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• 2000

In regression model, we estimate the unknown parameters by using various methods. There are the least squares method which is the most general, the least absolute deviation method, the regression quantile method and the asymmetric least squares method. In this paper, we will compare each others with two cases: firstly the theoretical comparison in the asymptotic sense and then the practical comparison using Monte Carlo simulation for a small sample size.

• Proceedings of the Korea Society for Simulation Conference
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• 2000.11a
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• pp.6-10
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• 2000

In regression model we estimate the unknown parameters using various methods. There are the least squares method which is the most general, the least absolute deviation, the regression quantile and the asymmetric least squares method. In this paper, we will compare each others with two case: to begin with the theoretical comparison in the asymptotic sense, and then the practical comparison using Monte Carlo simulation for a small sample size.

• Journal of the Korean Data and Information Science Society
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• v.1
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• pp.47-57
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• 1990

A method which determines the number of replications in the simulation is proposed, particularly for small-sample comparison of estimators. This method takes the smallest number of replications that makes the difference of mean square errors be statistically significant and provides an efficient algorithm for calculating the standard error of the mean square error. Two examples are illustrated, the first one is on comparison of mean and median ; the second, the Kaplan-Meier type and Buckley-James type estimators of a quantile function with censored data.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.5
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• pp.61-72
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• 2020

The purpose of this study is to investigate the determinants of the capital structure of firms operating in a developing economy, Pakistan. The quantile regression method is applied on a sample of 183 non-financial companies listed on the Pakistan Stock Exchange during the period of 2008-2017. Specifically, the empirical analysis focuses on changes in the coefficients of the determinants according to the leverage ratio quantiles of the examined listed firms. The findings show that the capital structure of Pakistan listed firms differs between firms in different quantiles of leverage. These differences are significant with the sign of explanatory variables changes with the level of leverage. The research result found tangibility, profitability and age to be positively related to leverage among listed firms in Pakistan. However, size, liquidity and non-debt tax shield (NDTS) are negatively related to leverage. A firm's growth and risk are found to be insignificant predictors of capital structure in Pakistan listed firms. Moreover, the study also found a significant impact of industry characteristic on leverage. The findings of this study indicate that an individual firm's finance policy needs to be responsive to the firm's characteristics and should match with the different borrowing requirements of listed firms.

• Journal of Home Health Care Nursing
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• v.18 no.2
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• pp.81-87
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• 2011

Purpose: The aim of this study was to estimate optimization model of stay time in EMC. Methods: Data were collected at an EMC in a hospital using medical records from June to August in 2007. The sample size was 8,378. The data were structured by stay time for doctor visit, decision making, and discharge from EMC. Descriptive statistics were used to find out general characteristics of patients. Average mean and quantile regression models were adopted to estimate optimized stay time in EMC. Results: The stay times in EMC were highly skewed and non-normal distributions. Therefore, average mean as an indicator of optimal stay time was not appropriate. The total stay time using conditional quantile regression model was estimated about 110 min, that was about 166 min shorter than estimated time using average mean. Conclusion: According to these results, we recommend to use a conditional quantile regression model to estimate optimal stay time in EMC. We suggest that this results will be used to develop a guideline to manage stay time more effectively in EMC.

• Annals of Occupational and Environmental Medicine
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• v.35
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• pp.23.1-23.14
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• 2023

Background: Exposure to heavy metals is a public health concern worldwide. Previous studies on the association between heavy metal exposure and neurobehavioral functions in children have focused on single exposures and clinical manifestations. However, the present study evaluated the effects of heavy metal complex exposure on subclinical neurobehavioral function using a Korean Computerized Neurobehavior Test (KCNT). Methods: Urinary mercury, lead, cadmium analyses as well as symbol digit substitution (SDS) and choice reaction time (CRT) tests of the KCNT were conducted in children aged between 10 and 12 years. Reaction time and urinary heavy metal levels were analyzed using partial correlation, linear regression, Bayesian kernel machine regression (BKMR), the weighted quantile sum (WQS) regression and quantile G-computation analysis. Results: Participants of 203 SDS tests and 198 CRT tests were analyzed, excluding poor cooperation and inappropriate urine sample. Partial correlation analysis revealed no association between neurobehavioral function and exposure to individual heavy metals. The result of multiple linear regression shows significant positive association between urinary lead, mercury, and CRT. BMKR, WQS regression and quantile G-computation analysis showed a statistically significant positive association between complex urinary heavy metal concentrations, especially lead and mercury, and reaction time. Conclusions: Assuming complex exposures, urinary heavy metal concentrations showed a statistically significant positive association with CRT. These results suggest that heavy metal complex exposure during childhood should be evaluated and managed strictly.

• Human Ecology Research
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• v.60 no.2
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• pp.289-302
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• 2022

This study examined the effect of private educational expenditure on adolescent depression and somatic symptoms. The sample comprised 2,589 first-grade middle-school students who completed the 2018 Korea Children and Youth Panel Survey. Data were analyzed using ANOVA (the generalized linear model), multiple regression, and quantile regression analysis. The principal results were as follows. First, 15.15% of adolescents reported depression symptoms, and 15.57% reported somatic symptoms. Second, levels of depression were significantly different among classes with a different level of private educational expenditure. Third, depression level was significantly negatively associated with private educational expenditure, in that the higher the private educational expenditure, the lower the depression level. Fourth, the effect of private educational expenditure on adolescent depression was significant at the 70~90th quantile regression, suggesting that private educational expenditure was associated with a higher level of depression symptoms. The results indicate that private education was viewed as a consumption commodity rather than a complementary educational practice or investment in human capital. Private education as a commodity might induce the highly developed and costly private education market. In turn, there is an increased financial burden for education at one end of the social-economic continuum and depression caused by relative deprivation at the other end.

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.163-169
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• 2006

This paper deals with the comparison of parameter estimation methods in a 3-parameter Kappa distribution which is sometimes used in flood frequency analysis. The method of moment estimation(MME), L-moment estimation(L-ME), and maximum likelihood estimation(MLE) are applied to estimate three parameters. The performance of these methods are compared by Monte-carlo simulations. Especially for computing MME and L-ME, ike dimensional nonlinear equations are simplied to one dimensional equation which is calculated by the Newton-Raphson iteration under constraint. Based on the criterion of the mean squared error, the L-ME is recommended to use for small sample size $(n\leq100)$ while MLE is good for large sample size.