• Journal of the Korean Statistical Society
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• v.15 no.2
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• pp.97-106
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• 1986

Assume that $X_1, X_2, \cdots, X_N$ are iid p-dimensional normal random vectors ($p \geq 3$) with unknown covariance matrix. The problem of estimating multivariate normal mean vector in an empirical Bayes situation is considered. Empirical Bayes estimators, obtained by Bayes treatmetn of the covariance matrix, are presented. It is shown that the estimators are minimax, each of which domainates teh maximum likelihood estimator (MLE), when the loss is nonsingular quadratic loss. We also derive approximate credibility region for the mean vector that takes advantage of the fact that the MLE is not the best estimator.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.330-335
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• 1995

The rebustness of one sample Chi-square test for multivariate normal mean vector is investigated when the multivariate normal population is mixed with another multivariate normal population with differing in the mean vector. Explicit expressions for the level of significance and power of the test are derived. Some numerical results indicate that the Chi-square test procedure is quite robust against slight mixtures of multivariate normal populations differing in location parameters.

• Bulletin of the Korean Mathematical Society
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• v.35 no.3
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• pp.547-557
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• 1998

In this paper, we study submanifolds in the Euclidean space with parallel normal mean curvature vectorand special quadric representation. Especially we give a complete classification result relative to surfaces satisfying these conditions.

• Journal of applied mathematics & informatics
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• v.40 no.3_4
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• pp.587-601
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• 2022

We find the characterizations of the curvatures of Legendre curves and magnetic curves in Kenmotsu manifolds with C-parallel and C-proper mean curvature vector fields in the tangent and normal bundles. Finally, an illustrative example is presented.

• Honam Mathematical Journal
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• v.26 no.2
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• pp.219-236
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• 2004

In this paper, the problem of estimating a p-variate ($p{\geq}4$) normal mean vector is considered in a decision-theoretic setup. Using a simple property of the noncentral chi-square distribution, a sequence of smooth estimators dominating the Lindley type estimator has been produced and each improved estimator is better than previous one.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.11-19
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• 2002

In this paper, the problem of estimating a p-variate $(p\geq4)$ normal mean vector in a decision-theoretic setup is considered. Using a technique of Guo and Pal (1992), a sequence of estimators dominating the Lindley type estimator is derived and each improved estimator is better than the previous one.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.463-472
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• 2005

In this paper, the problem of estimating a p-variate (p $\ge$4) normal mean vector is considered in decision-theoretic set up. Using a simple property of the noncentral chi-square distribution, a sequence of estimators dominating the Lindley type estimator with the cases of unknown covariance matrices has been produced and each improved estimator is better than previous one.

• Journal of the Chungcheong Mathematical Society
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• v.26 no.1
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• pp.29-36
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• 2013

In the treatment of the sequential beta-protection procedure, we define the reasonable stopping time and investigate that for the stopping time Wijsman's requirements, coverage probability and beta-protection conditions, are satisfied in the estimation for the mean vector ${\mu}$ by the sample from the multivariate normal distributed population with unknown mean vector ${\mu}$ and a positive definite variance-covariance matrix ${\Sigma}$.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.159-164
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• 2000

In the study of the isotonic regression problem, several procedures for testing the homogeneity of a normal mean vector versus order restricted alternatives have been proposed since Barlow's trial(1972). In this paper, we consider the problem of testing the equality of two order restricted normal mean vectors based on the likelihood ratio principle.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.3
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• pp.451-457
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• 2009

Most of objects in computer graphics may be represented by a form of mesh. The exact computation of vertex normal vectors is essential for user to apply a variety of geometric operations to the mesh and get more realistic rendering results. Most of the previous algorithms used a weight which resembles a local geometric property of a vertex of a mesh such as the interior angle, the area, and so on. In this paper, we propose an efficient algorithm for computing the normal vector of a vertex in meshes. Our method uses the conformal mapping which resembles synthetically the local geometric properties, and the mean value coordinates which may smoothly represent a relationship with the adjacent vertices. It may be confirmed by experiment that the normal vector of our algorithm is more exact than that of the previous methods.