• Communications for Statistical Applications and Methods
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• v.15 no.5
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• pp.783-791
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• 2008

Estimation procedure of the finite population proportion and distribution function is considered. Based on a logistic regression model, an approximately model- optimal estimator is defined and conditions for the estimator to be design-consistent are given. Simulation study shows that the model-optimal design-consistent estimator defined under a logistic regression model performs well in estimating the finite population distribution function.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.211-218
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• 1998

This paper deals with the problem of obtaining some Bayes estimators of exponential reliability in a time censored sampling with incomplete information. Some Bayes estimators are proposed and studied under squared error loss and Harris loss.

• Structural Engineering and Mechanics
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• v.8 no.4
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• pp.421-438
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• 1999

In this paper a practical limit load estimating procedure is proposed for general pipe-elbow structures subjected to complex loading (in-plane and out-of-plane bending, internal pressure and axial force). The explicit calculating formulae are presented on the basis of theoretical analysis combined with numerical simulation. Von Mises' yield criterion is adopted in both analytical and numerical calculation. The finite element examination shows that the method provides a simple but satisfactory prediction of pipe structures in engineering plastic analysis.

• Journal of the Korean Statistical Society
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• v.16 no.2
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• pp.102-112
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• 1987

This paper treats the problem of estimating an arbitrary parametric function in the case when the parameter and sample spaces are countable and the decision space is arbitrary. Using the notions of a stepwise Bayesian procedure and finite admissibility, a theorem is proved. It shows that under some assumptions, every finitely admissible estimator is unique stepwise Bayes with respect to a finite or countable sequence of mutually orthogonal priors with finite supports. Under an additional assumption, it is shown that the converse is true as well. The first can be also extended to the case when the parameter and sample space are arbitrary, i.e., not necessarily countable, and the underlying probability distributions are discrete.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.973-981
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• 2010

Quantile regression investigates the quantiles of the conditional distribution of a response variable given a set of covariates. M-quantile regression extends this idea by a "quantile-like" generalization of regression based on influence functions. In this paper we propose a new method of estimating M-quantile regression functions, which uses kernel machine technique. Simulation studies are presented that show the finite sample properties of the proposed M-quantile regression.

• Journal of the Korean Statistical Society
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• v.26 no.3
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• pp.309-317
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• 1997

A sequential procedure for estimating a linear function of two normal means which satisfies the two requirements, i.e. one is a condition of coverage probability, the other is a condition of $\beta$-protection, is proposed when the variances are unknown and not necessarily equal. We give asymptotic behaviors of the proposed stopping time.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.446-450
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• 1987

In this paper, overdetermined method is used for high resolution spectral estimation in case of short data record length. To reduce the computational effort and to obtain recursive form of estimation algorithm, we modify data matrix to have near-Toeplitz structure. Then, new recursive algorithm is derived in the form of fast Kalman algorithm. Two stage procedure is used for the estimation of ARMA parameters. First AR parameters are estimated by using overdetermined modified Yule-walker equation, and then MA parameters are implicitly estimated by estimating numerator spectral coefficients(NS).

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.1
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• pp.65-82
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• 1996

This paper proposes a sequential procedure which can be used to determine a truncation point and run length to reduce or remove bias owing to artificial startup conditions in simulations aimed at estimating steady-state behavior. It is based on the idea of Lyapunov exponent in chaos theory. The performance measures considered are relative bias, coverage, estimated relative half-width of the confidence interval, and mean amount of deleted data.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.297-312
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• 2000

A three stage sampling procedure designed to estimate the difference betweentwo normal means is proposed and evaluated within a unified decision-theoretic framework. Both point and fixed-width confidence interval estimation are combined in a single decision rule to make full use of the available data. Adjustments to previous solutions focusing on only one of the latter objectives are indicated. The sensitivity of the confidence interval for detecting shifts in true mean difference is also investigated Numerical and simulation studies are presented to supplement the theoretical results.

• Communications for Statistical Applications and Methods
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• v.2 no.1
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• pp.145-154
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• 1995

Empirical Bayes approach is applied to estimation of the binomial parameter when there is a cost for observations. Both the sample size and the decision rule for estimating the parameter are determined stochastically by the data, making the result more useful in applications. Our empirical Bayes problems with non-iid components are compared to the usual empirical Bayes problems with iid components. The asymptotic optimal procedure with a computer simulation is given.