• Journal of the Korean Statistical Society
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• 제21권2호
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• pp.127-137
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• 1992

In this paper we investigate scaling limits for associated random measures satisfying some moment conditions. No stationarity is required. Our results imply an improvement of a central limit theorem of Cox and Grimmett to associated random measure and an extension to the nonstationary case of scaling limits of Burton and Waymire. Also we prove an invariance principle for associated random measures which is an extension of the Birkel's invariance principle for associated process.

• 충청수학회지
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• 제13권2호
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• pp.19-27
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• 2001

In this paper we study another kind of the large deviation property, i.e. moderate deviation type for random amount of random measures on $R^d$ about a Poisson point process and a Poisson center cluster random measure.

• 한국지능시스템학회논문지
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• 제11권4호
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• pp.307-310
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• 2001

확률 공간에서 퍼지 집합의 특징성의 측도에 관하여 소개한다. 이에 관한 Yager의 결과들을 개선 및 일반화하고 랜덤샘플에 의한 퍼지 집합의 특징성의 퍼지측도에 대한 극한에 대하여 연구하고자 한다.

• 대한수학회지
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• 제54권3호
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• pp.1049-1061
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• 2017

The paper presents a characterization of stable random measures, giving a canonical form of their Laplace transform. Domain of attraction of stable random measures is concerned in a theorem showing that a random measure belongs to domain of attraction of any stable random measures if and only if it varies regularly at infinity.

• 대한수학회논문집
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• 제17권1호
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• pp.71-80
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• 2002

We give a formulation of the large deviation property for rescalings of random measures on the d-dimensional Euclidean space R$^{d}$ . The approach is global in the sense that the objects are Radon measures on R$^{d}$ and the dual objects are the continuous functions with compact support. This is applied to the cluster random measures with Poisson centers, a large class of random measures that includes the Poisson processes.

• Journal of the Korean Data and Information Science Society
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• 제28권3호
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• pp.515-524
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• 2017

이 연구에서는 분류를 위한 RF (random forest)의 크기 결정에 유용한 승리표차 MV (margin of victory)에 기반한 불일치 측도를 제안하고자 한다. 여기서 MV는 현재의 RF에서 1등과 2등을 차지하는 집단이 무한 RF에서 차지하는 승리표차이다. 구체적으로 -MV가 양수이면 현재와 무한 RF 사이에 1등과 2등인 집단에서 불일치가 생긴다는 점에 착안하여, max(-MV, 0)을 하나의 불일치 측도로 제안한다. 이 불일치 측도에 근거하여 RF의 크기 결정에 적절한 진단통계량을 제안하며, 또한 이 통계량의 이론적인 점근분포를 유도한다. 마지막으로 이 통계량을 최근에 제안된 진단통계량들과 소표본 하에서 성능을 비교하는 모의실험을 실행한다.

• 대한수학회지
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• 제42권2호
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• pp.269-289
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• 2005

An infinite system of stochastic differential equations (SDE)driven by Brownian motions and compensated Poisson random measures for the locations and weights of a collection of particles is considered. This is an analogue of the work by Kurtz and Xiong where compensated Poisson random measures are replaced by white noise. The particles interact through their weighted measure V, which is shown to be a solution of a stochastic differential equation. Also a limit theorem for system of SDE is proved when the corresponding Poisson random measures in SDE converge to white noise.

• Journal of applied mathematics & informatics
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• 제3권1호
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• pp.89-100
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• 1996

In this paper we investigate a limit theorem for a non-statioary d-parameter array of associated random variables applying the criterion of the tightness condition in Donsker, M[1951]. Our re-sults imply an extension to the nonstatioary case of Convergence of Probability Measure of billingsley. P[1986]. and analogous results for the d-dimensional associated random measure. These results are also applied to show a new limit theorem for Poisson cluster random mea-sures.

• 대한기계학회논문집A
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• 제24권9호
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• pp.2283-2291
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• 2000

The neural network method is applied to automatically measure the crack opening load under random loading. The crack opening results obtained are compared with the visual measured results. Fatigue crack growth under random loading is predicted using the crack opening data measured by the neural network method, and the prediction results are compared with experimental ones. It is found that the neural network method can be successfully applied to consistently measure the crack opening load under random loading and also gives some results different from the results by visual measurement.

• 호남수학학술지
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• 제24권1호
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• pp.121-130
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• 2002

In this paper we prove a functional central limit theorem for a field $\{X_{\underline{j}}:{\underline{j}}{\in}Z_+^d\}$ of nonstationary associated random variables with $EX{\underline{j}}=0,\;E{\mid}X_{\underline{j}}{\mid}^{r+{\delta}}<{\infty}$ for some $r>2,\;{\delta}>0$and $u(n)=O(n^{-{\nu}})$ for some ${\nu}>0$, where $u(n):=sup_{{\underline{i}}{\in}Z_+^d{\underline{j}}:{\mid}{\underline{j}}-{\underline{i}}{\mid}{\geq}n}{\sum}cov(X_{\underline{i}},\;X_{\underline{j}}),\;{\mid}{\underline{x}}{\mid}=max({\mid}x_1{\mid},{\cdots},{\mid}x_d{\mid})\;for\;{\underline{x}}{\in}{\mathbb{R}}^d$. Our investigation implies and analogous result in the case associated random measure.