• Health Policy and Management
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• v.25 no.3
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• pp.240-251
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• 2015

Background: Dentist's income is quite variable. We investigate the factors underlying the distribution of dental revenue and dentist income. Methods: Financial and structural variables of private dental practices(N=13,967) were examined with 2010 Economic Census microdata which include non-insurance revenue. We conducted quantile regression method(QRM) and ordinary least square(OLS) in treating skewness and heteroskedasticity of distributions. The effective estimation for the upper and lower range of distribution becomes possible by QRM. Results: Mid-career dentists are shown to have higher revenue and income. Male dentists achieve the higher revenue and income than female dentists in all quantiles. Group practices show lower income per owner than solo practices significantly. The revenue and income are increased with increasing size of clinics. The high cost in renting the clinic office is found to have a big positive effect on the revenue but a little positive effect on the income. Interestingly the density of dentists shows negative effect on the lowest quantile of the revenue but positive effect on the highest quantile. The lowest quantile of the revenue in the capital areas have the relatively high revenue. The lowest quantile of the income in metropolitan city show higher income than those in other areas significantly. Conclusion: The suggested QRM is shown to have more effective and efficient tool in finding out determinants of dentists' revenue and income of our concern. The results of this study are expected to be employed for dentists preparing for the opening practices in their organizational settings and locational selections. The distributional efficiency of dental human resources could be accomplished if policy makers guide dentists with this knowledge.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.625-636
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• 2013

In this paper we study four kernel machines for estimating expected shortfall, which are constructed through combinations of support vector quantile regression (SVQR), restricted SVQR (RSVQR), least squares support vector machine (LS-SVM) and support vector expectile regression (SVER). These kernel machines have obvious advantages such that they achieve nonlinear model but they do not require the explicit form of nonlinear mapping function. Moreover they need no assumption about the underlying probability distribution of errors. Through numerical studies on two artificial an two real data sets we show their effectiveness on the estimation performance at various confidence levels.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.807-818
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• 2005

In this paper we propose an estimation method for regression quantiles with left-truncated and right-censored data. The estimation procedure is based on the weight determined by the Kaplan-Meier estimate of the distribution of the response. We show how the proposed regression quantile estimators perform through analyses of Stanford heart transplant data and AIDS incubation data. We also investigate the effect of censoring on regression quantiles through simulation study.

• Communications for Statistical Applications and Methods
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• v.18 no.4
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• pp.457-465
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• 2011

Sample entropy (Vasicek, 1976) has poor performance, and several nonparametric entropy estimators have been proposed as alternatives. In this paper, we consider a piecewise uniform density function based on quantiles, which enables us to evaluate entropy in each interval, and study the poor performance of the sample entropy in terms of the poor estimation of lower and upper quantiles. Then we propose some improved entropy estimators by simply modifying the quantile estimators, and compare their performances with some existing estimators.

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1095-1106
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• 2016

The quantile regression method proposed by Koenker et al. (1978) focuses on conditional quantiles given by independent variables, and analyzes the relationship between response variable and independent variables at the given quantile. Considering the linear programming used for the estimation of quantile regression coefficients, the model fitting job might be difficult when large data are introduced for analysis. Therefore, dimension reduction (or variable selection) could be a good solution for the quantile regression of large data sets. Regression tree methods are applied to a variable selection for quantile regression in this paper. Real data of Korea Baseball Organization (KBO) players are analyzed following the variable selection approach based on the regression tree. Analysis result shows that a few important variables are selected, which are also meaningful for the given quantiles of salary data of the baseball players.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1878-1884
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• 2023

To lay a solid basis for prosperity and competitiveness, countries should achieve balance in the three fundamental aspects: energy availability, energy affordability and ecological balance. Nuclear energy has attracted international interest as one of the most crucial environmental quality strategies. The objective of this study is to analyze the non-linear link between nuclear energy and environmental quality in the top-10 nuclear energy consumer countries (USA, China, Russia, France, Canada, Spain, Sweden, South Korea, Ukraine, and Germany). Earlier research employed panel data methodologies to examine the linkage between nuclear energy and the environment, despite the fact that many nations did not independently demonstrate such a correlation. On the alternative, this study uses a novel approach known as 'Quantile-on-Quantile,' which allows for the analysis of time-series dependence in each country by giving universal yet country-specific insights into the relationship between the variables. Estimates show that the consumption of nuclear energy improves environmental quality by lowering ecological footprint in the majority of the nations studied at certain quantiles of data. Moreover, the data demonstrate that the degree of asymmetries between our variables changes by nation, emphasizing the importance of policymakers exercising caution when adopting nuclear energy and environmental quality regulations.

• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.179-186
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• 1996

Bioequivanence models arise typically in bioassays when new preparations are compared against standard ones by means of responses on some biological organisms. Relative potency measures provide nice interpretations for such bioequivalence and their estimation constitutes the prime interest of such studies. A conditional quantile process based on the k-nearest neighbor method is proposed for this purpose. An alternative procedure based on Kolmogrov-Smirnov type estimator has also been considered along with. ARIC ultrasound data are analyzed as examples.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.25-32
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• 1999

A single outlier can influence on the least squares estimators and can invalidate analysis based on these estimators. The Cook's statistic has been introduced to measure influence of individual data point on parameter estimation and the quantile of the F distribution is recommended as a reference value. but in practice subjective judgement is applied in the choice of appropriate quantile. A simple reference value is introduced in this paper which is developed by approximating conditional quantities of Cook's measure. The performance of the proposed criterion is evaluated through analysis of real data set.

• Water for future
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• v.50 no.1
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• pp.18-25
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• 2017

• International Journal of Reliability and Applications
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• v.9 no.1
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• pp.53-70
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• 2008

In this paper, we discuss the plethora of uses for the software package R, and focus specifically on its helpful applications in reliability data analyses. Examples are presented; including the R coding protocol, R code, and plots for various statistical as well as reliability analyses. We explore Kaplan-Meier estimates and maximum likelihood estimation for distributions including the Weibull. Finally, we discuss future applications of R, and usages of quantile regression in reliability.