• Communications for Statistical Applications and Methods
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• v.4 no.2
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• pp.541-548
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• 1997

In regression model with nested error structure interval estimations on regression coefficients in different stages are proposed. Ordinary least square estimators and generalized least square estimators of the regression coefficients in this model are derived for between and within group model. The confidence intervals are dervied by using independent idstributional properties between regression coefficient estimators and quadratic froms obtained from the model.

• Communications for Statistical Applications and Methods
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• v.6 no.2
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• pp.367-374
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• 1999

Biological specimens contain several components and multilinear models are very useful in analyzing these data. After fitting the model the number of components are determined by the change of mean squared error however this method is quite rule of thumb. in this paper we suggest a measure to decide the number of components based on the relative change of to mean squared error. Simulations are done and applications to real data set are given as illustrations.

• Journal of the Korean Statistical Society
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• v.25 no.4
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• pp.557-566
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• 1996

The aim of this paper is to find a suitable design which minimizes the expected discrepancy: in fitting a first order model fearing quadratic terms as bias where there are more than two correlated responses. Kim and Draper(1994) discussed about choosing a design for straight line fits to two correlated responses The general case with r responses is examined here and the result is applied to a specific case to help understandings.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.285-292
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• 1999

In this paper we propose the several estimators of the Lorenz curve in the Pareto distribution and obtain the bias and the mean squared error for each estimator. We compare the proposed estimators with the uniformly minimum variance unbiased estimator (UMVUE) and the maximum likelihood estimator (MLE) in terms of the mean squared error (MSE) through Monte Carlo methods and discuss the results.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.177-183
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• 1997

A data-adaptive order selection procedure is proposed for local polynomial nonparametric regression. For each given polynomial order, bias and variance are estimated and the adaptive polynomial order that has the smallest estimated mean squared error is selected locally at each location point. To estimate mean squared error, empirical bias estimate of Ruppert (1995) and local polynomial variance estimate of Ruppert, Wand, Wand, Holst and Hossjer (1995) are used. Since the proposed method does not require fitting polynomial model of order higher than the model order, it is simpler than the order selection method proposed by Fan and Gijbels (1995b).

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.75-78
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• 2003

Those who are interested in making inferences concerning linear combination of variance components in a simple linear regression model with unbalanced nested error structure can use the confidence intervals proposed in this paper. Two approximate confidence intervals for the sum of two variance components in the model are proposed. Simulation study is peformed to compare the methods.

• Journal of the Korean Statistical Society
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• v.8 no.1
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• pp.37-64
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• 1979

Criteria and designs are developed for estimating derivatives of P-variable second order polynomial response surfaces. The basic criterion used is mean square error of the estimated derivative, averaged over all directions and then averaged over a region of interest. A new design concept called slope-rotatability is introduced. A simplex optimization program is used to find minimum mena square error designs for the two variable case for $6 \leq N \leq 12$.

• Journal of the Korean Statistical Society
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• v.36 no.3
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• pp.397-410
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• 2007

For the two groups data from multivariate binomial distribution, we consider a bootstrap approach to inferring the simultaneous confidence level and its standard error of a collection of the dependent confidence intervals for the difference of proportions with an experimentwise error rate at the a level are presented. The bootstrap method is used to estimate the simultaneous confidence probability for the difference of proportions.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.209-217
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• 2001

We obtain the approximate maximum likelihood estimators (AMLEs) for the scale and location parameters $\theta$ and $\mu$ in the three-parameter Weibull distribution based on Type-II censored samples. We also compare the AMLEs with the modified maximum likelihood estimators (MMLEs) in the sense of the mean squared error (MSE) based on complete sample.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.52-62
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• 1995

Consider an estimation problem under squared error loss in a family of non-regular densities with both terminals of the support being decreasing functions of an unknown parameter. Using Karlin's(1958) technique, sufficient conditions are given for generalized Bayes estimators to be admissible for estimating an arbitrarily positive, monotone parametric function and then treat some examples which illustrate our results.