• Proceedings of the Korean Association for Survey Research Conference
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• 2006.12a
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• pp.67-83
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• 2006

In successive sampling on two occasions the problem of estimating a finite population quantile has been considered. The theory developed aims at providing the optimum estimates by combining (i) three double sampling estimators viz. ratio-type, product-type and regression-type, from the matched portion of the sample and (ii) a simple quantile based on a random sample from the unmatched portion of the sample on the second occasion. The approximate variance formulae of the suggested estimators have been obtained. Optimal matching fraction is discussed. A simulation study is carried out in order to compare the three estimators and direct estimator. It is found that the performance of the regression-type estimator is the best among all the estimators discussed here.

• Journal of the Korean Statistical Society
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• v.36 no.4
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• pp.543-556
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• 2007

In successive sampling on two occasions the problem of estimating a finite population quantile has been considered. The theory developed aims at providing the optimum estimates by combining (i) three double sampling estimators viz. ratio-type, product-type and regression-type, from the matched portion of the sample and (ii) a simple quantile based on a random sample from the unmatched portion of the sample on the second occasion. The approximate variance formulae of the suggested estimators have been obtained. Optimal matching fraction is discussed. A simulation study is carried out in order to compare the three estimators and direct estimator. It is found that the performance of the regression-type estimator is the best among all the estimators discussed here.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.15-27
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• 2001

One proposal is made for constructing nonparametric estimator of slope parameters in a regression model under symmetric error distributions. This estimator is based on the use of idea of Johns for estimating the center of the symmetric distribution together with the idea of regression quantiles and regression trimmed mean. This nonparametric estimator and some other L-estimators are studied by Monte Carlo.

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.793-802
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• 2012

One proposal is made to construct a nonparametric estimator of slope parameters in a regression model under symmetric error distributions. This estimator is based on the use of the idea of minimizing approximate variance of a proposed estimator using regression quantiles. This nonparametric estimator and some other L-estimators are studied and compared with well known M-estimators through a simulation study.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.1017-1024
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• 2003

An estimation problem of treatment effect for bivariate censored survival data is considered under location shift model between two sample. The proposed estimator is very intuitive and can be obtained in a closed form. Asymptotic results of the proposed estimator are discussed and simulation studies are performed to show the strength of the proposed estimator.

• The Korean Journal of Applied Statistics
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• v.7 no.2
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• pp.173-182
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• 1994

By applying slope estimator under the arbitrary error distributions proposed by Han(1993), if we define regression quantiles to give upper and lower trimming part and blocks of data, we show the proposed slope estimator has asymptotically efficient slope estimator when the number of regression quantiles to from blocks of data goes to sufficiently large.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.677-684
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• 2014

Support vector quantile regression (SVQR) is capable of providing more complete description of the linear and nonlinear relationships among random variables. To improve the estimation performance of SVQR we propose to use SVQR ensemble with bagging (bootstrap aggregating), in which SVQRs are trained independently using the training data sets sampled randomly via a bootstrap method. Then, they are aggregated to obtain the estimator of the quantile regression function using the penalized objective function composed of check functions. Experimental results are then presented, which illustrate the performance of SVQR ensemble with bagging.

• 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.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.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1441-1448
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• 2008

We propose the modified quantile-quantile (Q-Q) plot using the approximate maximum likelihood estimators and the modified normalized sample Lorenz curve (NSLC) plot for the extreme value distribution based on multiply Type-II censored samples. Using two example data sets, we picture the modified Q-Q plot and the modified NSLC plot.