• Journal of the Korean Statistical Society
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• 제27권4호
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• pp.459-469
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• 1998

Balanced half sample method is a simple variance estimation method for complex sampling designs. Since it is simple and flexible, it has been widely used in large scale sample surveys. However, the usual BHS method overestimate the true variance in without replacement sampling and two-stage cluster sampling. Focusing on this point , we proposed an unbiased BHS variance estimator in a stratified two-stage cluster sampling and then described an implementation method of the proposed estimator. Finally, partially BHS design is explained as a tool of reducing the number of replications of the proposed estimator.

• Journal of the Korean Statistical Society
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• 제22권1호
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• pp.1-12
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• 1993

An approach to make the information bound sharper in median-unbiased estimation, based on an analogue of the Cramer-Rao inequality developed by Sung et al. (1990), is introduced for continuous densities with a nuisance parameter by considering information quantities contained both in the parametric function of interest and in the nuisance parameter in a linear fashion. This approach is comparable to that of improving the information bound in mean-unbiased estimation for the case of two unknown parameters. Computation of an optimal weight corresponding to the nuisance parameter is also considered.

• 전기의세계
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• 제22권2호
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• pp.51-56
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• 1973

The comparative study of maximum likelihood estimation with Bayesian approach was made by statistical & computational methods in center of a priori information of static systems and the effect of a priori information on the accuracy of the estimatiion was also analyzed. Through the numerical computations of some examples by digital computer, we concluded that maximum likelihood method is better than Bayesian estimation except for almost certain a priori informations. The study may therefore contribute in identification problems of dynamical systems connected with a priori informations.

• Communications for Statistical Applications and Methods
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• 제8권1호
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• pp.87-96
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• 2001

We consider the empirical Bayes estimation problems with the binomial and normal components when the prior distributions are unknown but are assumed to be in certain families. There may be the families of all distributions on the parameter space or subfamilies such as the parametric families of conjugate priors. We treat both cases and establish the asymptotic optimality for the corresponding decision procedures.

• Communications for Statistical Applications and Methods
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• 제2권2호
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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.

• Journal of the Korean Statistical Society
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• 제25권3호
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• pp.359-368
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• 1996

The maximum likelihood estimation is discussed for the NLAR model with Laplacian marginals. Since the explicit form of the estimates cannot be obtained due to the complicated nature of the likelihood function we utilize the automatic computer optimization subroutine using a direct search complex algorithm. The conditional least square estimates are used as initial estimates in maximum likelihood procedures. The results of a simulation study for the maximum likelihood estimates of the NLAR(1) and the NLAR(2) models are presented.

• Communications for Statistical Applications and Methods
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• 제7권3호
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• pp.767-772
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• 2000

Zellner(1994) introduced the notion of a balanced loss function in the context of a general liner model to reflect both goodness of fit and precision of estimation. We study the perspective of unifying a variety of results both frequentist and Bayesian from Poisson distributions. We show that frequentist and Bayesian results for balanced loss follow from and also imply related results for quadratic loss functions reflecting only precision of estimation. Several examples are given for Poisson distribution.

• Communications for Statistical Applications and Methods
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• 제2권2호
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• pp.115-125
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• 1995

This paper deals with the Bayesian estimation for the NLAR(1) model with Laplacian marginals. Assuming the independent uniform priors for two parameters of the NLAT(1) model, the griddy Gbbs sampler by Ritter and Tanner(1992) is used to obtain the Bayesian estimates. Random numbers generated form the uniform priors ate used as the grids for each parameter. Some simulations are conducted and compared with the maximum likelihood estimation result.

• Journal of the Korean Statistical Society
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• 제32권4호
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• pp.385-399
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• 2003

We first introduce the quasi-score estimating function and applied the quasi-score estimating function to nonlinear time series models. We proposed the M quasi-score estimating functions bounded functions for the quasi-score estimating functions. Also, we investigated the asymptotic properties of quasi-likelihood estimators and M quasi-likelihood estimators. Simulation results show that the M quasi-likelihood estimators work better than the least squares estimators under the heavy-tailed distributions

• Communications for Statistical Applications and Methods
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• 제19권6호
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• pp.877-884
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• 2012

Phase I trials determine the maximum tolerated dose(MTD) and the recommended dose(RD) for subsequent Phase II trials. In this paper, a MTD estimation method applied to a biased coin design is proposed for Phase I Clinical Trials. The suggested MTD estimation method is compared to the SM3 method and the NM method (Lee and Kim, 2012) using a Monte Carlo simulation study.