• Honam Mathematical Journal
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• v.45 no.3
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• pp.503-512
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• 2023

In the present article, we introduce the concepts of strongly asymptotically lacunary equivalence, asymptotically statistical equivalence, and asymptotically lacunary statistical equivalence for sequences in g-metric spaces. We investigate some properties and relationships among these new concepts.

• Bulletin of the Korean Mathematical Society
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• v.35 no.4
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• pp.741-755
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• 1998

We prove the asymptotic lens equivalence in manifolds without conjugate points. By using this property we show that under a metric condition of asymptotically Euclidean and the curvature condition decaying faster than quadratic, any surface $(R^2,g)$ without conjugate points is Euclidean.

• Honam Mathematical Journal
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• v.43 no.2
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• pp.343-357
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• 2021

In the present paper, we introduce the concepts of $\mathcal{I}$-asymptotically statistical $\tilde{\phi}$-equivalence and $\mathcal{I}$-asymptotically lacunary statistical $\tilde{\phi}$-equivalence for triple sequences. Moreover, we give the relations between these new notions.

• Honam Mathematical Journal
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• v.43 no.1
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• pp.100-114
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• 2021

In this study, we present some asymptotical invariant and asymptotical lacunary invariant equivalence types for double sequences via ideals using modulus functions and investigate relationships between them.

• Proceedings of the Korean Reliability Society Conference
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• 2000.11a
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• pp.363-363
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• 2000

We derive an asymptotic relation between the scores for the censored and uncensored observations for the untied value case among uncensored observations. With this relation, we show that two types of the linear rank statistics which are based on any consistent estimates of the distribution function, are asymptotically equivalent. Also, we discuss another asymptotic equivalence considered by Cuzick (1985).

• Bulletin of the Korean Mathematical Society
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• v.55 no.4
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• pp.1303-1315
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• 2018

For principal component analysis (PCA) to efficiently analyze large scale matrices, it is crucial to find a few singular vectors in cheaper computational cost and under lower memory requirement. To compute those in a fast and robust way, we propose a new stochastic method. Especially, we adopt the stochastic variance reduced gradient (SVRG) method [11] to avoid asymptotically slow convergence in stochastic gradient descent methods. For that purpose, we reformulate the PCA problem as a unconstrained optimization problem using a quadratic penalty. In general, increasing the penalty parameter to infinity is needed for the equivalence of the two problems. However, in this case, exact penalization is guaranteed by applying the analysis in [24]. We establish the convergence rate of the proposed method to a stationary point and numerical experiments illustrate the validity and efficiency of the proposed method.

• Communications for Statistical Applications and Methods
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• v.16 no.6
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• pp.989-995
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• 2009

Basu et al. (1998) proposed the minimum divergence estimating method which is free from using the painful kernel density estimator. Their proposed class of density power divergences is indexed by a single parameter $\alpha$ which controls the trade-off between robustness and efficiency. In this article, (1) we introduce a new large class the minimum squared distance which includes from the minimum Hellinger distance to the minimum $L_2$ distance. We also show that under certain conditions both the minimum density power divergence estimator(MDPDE) and the minimum squared distance estimator(MSDE) are asymptotically equivalent and (2) in finite samples the MDPDE performs better than the MSDE in general but there are some cases where the MSDE performs better than the MDPDE when estimating a location parameter or a proportion of mixed distributions.

• The Mathematical Education
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• v.37 no.1
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• pp.1-13
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• 1998

In this paper, we have represented the efficient way how to enumerate the optimal number-right scores to adjust the item difficulty and to improve item discrimination. To estimate the optimal number-right scores in two equivalent math-tests by linear score equating a measurement error model was applied to the true scores observed from a pair of equivalent math-tests assumed to measure same trait. The model specification for true scores which is represented by the bivariate model is a simple regression model to inference the optimal number-right scores and we assume again that the two simple regression lines of raw scores and true scores are independent each other in their error models. We enumerated the difference between mean value of $\chi$＊ and ${\mu}$$\_$$\chi$/ and the difference between the mean value of y＊and a＋b${\mu}$$\_$$\chi$/ by making an inference the estimates from 2 error variable regression model. Furthermore, so as to distinguish from the original score points, the estimated number-right scores y’$\^$*/ as the estimated regression values of true scores with the same coordinate were moved to center points that were composed of such difference values with result of such parallel score moving procedure as above mentioned. We got the asymptotically normal distribution in Figure 5 that was represented as the optimal distribution of the optimal number-right scores so that we could decide the optimal proportion of number-right score in each item. Also by assumption that equivalence of two tests is closely connected to unidimensionality of a student’s ability. we introduce new definition of trait score to evaluate such ability in each item. In this study there are much limitations in getting the real true scores and in analyzing data of the bivariate error model. However, even with these limitations we believe that this study indicates that the estimation of optimal number right scores by using this enumeration procedure could be easily achieved.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.2
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• pp.149-156
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• 2011

In this paper, a methodology is proposed to improve the stress prediction of plates via Saint Venant's principle. According to Saint Venant's principle, the stress resultants can be used to describe linear elastic problems. Many engineering problems have been analyzed by Euler-Bernoulli beam(E-B) and/or Kirchhoff-Love(K-L) plate models. These models are asymptotically correct, and therefore, their accuracy is mathematically guaranteed for thin plates or slender beams. By post-processing their solutions, one can improve the stresses and displacements via Saint Venant's principle. The improved in-plane and out-of-plane displacements are obtained by adding the perturbed deflection and integrating the transverse shear strains. The perturbed deflection is calculated by applying the equivalence of stress resultants before and after post-processing(or Saint Venant's principle). Accuracy and efficiency of the proposed methodology is verified by comparing the solutions obtained with the elasticity solutions for orthotropic beams.