• Communications for Statistical Applications and Methods
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• 제9권1호
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• pp.129-139
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• 2002

Default Bayesian method for detecting the changes in sequences of independent exponential random variates and independent Poisson random variates is considered. Noninformative priors are assumed for all the parameters in both of change models. Default Bayes factors, AIBF, MIBF, FBF, to check whether there is any change or not on each sequence and the posterior probability densities of change at each time point are derived. Theoretical results discussed in this paper are applied to some numerical data.

• 대한수학회논문집
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• 제13권2호
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• pp.385-391
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• 1998

In this note, we study a weak law of large numbers for sequences of exchangeable random variables. As a special case, we have an extension of Kolmogorov's generalization of Khintchine's weak law of large numbers to i.i.d. random variables.

• 대한수학회지
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• 제57권3호
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• pp.553-570
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• 2020

In this paper, we establish complete convergence for sequences of extended independent random variables and arrays of rowwise extended independent random variables under sub-linear expectations in Peng's framework. The results obtained in this paper extend the corresponding ones of Baum and Katz [1] and Hu and Taylor [11] from classical probability space to sub-linear expectation space.

• 호남수학학술지
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• 제15권1호
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• pp.129-136
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• 1993

In this paper we present of a class infinite M A (moving-average) sequences of multivariate random vectors. We use the theory of positive dependence to show that in a variety of cases the classes of M A sequences are associated. We then apply the association to establish some probability bounds and moment inequalities for multivariate processes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권3호
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• pp.1670-1683
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• 2017

Mitotic event recognition is a crucial and challenging task in biomedical applications. In this paper, we introduce the slow feature analysis and propose a fully-automated mitotic event recognition method for cell populations imaged with time-lapse phase contrast microscopy. The method includes three steps. First, a candidate sequence extraction method is utilized to exclude most of the sequences not containing mitosis. Next, slow feature is learned from the candidate sequences using slow feature analysis. Finally, a hidden conditional random field (HCRF) model is applied for the classification of the sequences. We use a supervised SFA learning strategy to learn the slow feature function because the strategy brings image content and discriminative information together to get a better encoding. Besides, the HCRF model is more suitable to describe the temporal structure of image sequences than nonsequential SVM approaches. In our experiment, the proposed recognition method achieved 0.93 area under curve (AUC) and 91% accuracy on a very challenging phase contrast microscopy dataset named C2C12.

• 대한수학회보
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• 제30권2호
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• pp.167-170
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• 1993

In this paper, X, X$_{1}$, X$_{2}$, .. will denote any sequence of pairwise independent random variables with common distribution, and b$_{1}$, b$_{2}$.. will denote any sequence of constants. Using Chung [2, Theorem 4.2.5] and the same idea as in Chow and Robbins [1, Lemma 1 and 2] we have the following lemma.

• Journal of the Korean Data and Information Science Society
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• 제9권2호
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• pp.255-262
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• 1998

In this paper, we study the saddlepoint approximations for the ratio of independent random variables. In Section 2, we derive the saddlepoint approximation to the probability density function. In Section 3, we represent a numerical example which shows that the errors are small even for small sample size.

• 대한수학회보
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• 제43권3호
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• pp.551-558
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• 2006

We establish a weak law of large numbers for sequence of random elements with values in p-uniformly smooth Banach space. Our result is more general and stronger than some well-known ones.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 춘계학술대회 학술발표 논문집
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• pp.33-36
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• 1997

We prove a uniform strong law of large numbers for sequences of fuzzy random variables.

• 한국가금학회지
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• 제20권4호
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• pp.177-188
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• 1993

닭에서 적절한 성감별 방법을 개발하고 닭의 성분화 기작의 기초자료를 얻기 위하여 배아의 섬유아세포의 염색체를 분석하고, W 염색체 특이적인 반복염기서열과 50∼60％의 유사성을 보이는 random primer로 PCR 증폭을 실시하여 성을 판별하는 방법이 이용되었으며, 닭에서 성분화에 관련된 유전자를 분리하기 위해 W 염색체 특이적인 반복염기서열을 클로닝하였고, PCR을 이용하여 ZFY와 SRY 염기서열을 증폭하였다. 닭의 배아섬유세포의 염색체 분석 결과 Z 염색체와 W 염색체를 구분함으로써 배아의 성을 직접적으로 판별하는 것이 가능하였으며 , 암닭의 DNA를 Xho Ⅰ와 Eco RI로 절단하여 생성되는 band를 이용하여 성을 판별하는 것이 가능하였다. Xho Ⅰ와 Eco RI family를 클로닝하고, colony hybridization을 통해 Xho Ⅰ과 염기서열이 유사한 80∼100개의 clone을 동정하여, 이들 두 그룹간 DNA homology는 매우 유사하였다. 150개의 random primer 중 W 염색체 특이적인 반복 염기서열과 유사성을 보이는 primer 7개를 screening하였으며, 이 중 3개의 primer는 닭에서 자성과 웅성간의 차이를 나타내었다. 닭에서 성분화에 관련된 유전자를 동정하기 위하여 포유류의 ZFY와 SRY유전자의 PCR증폭을 실시하였다. ZFY를 증폭한 결과, 자성과 웅성간의 차이를 발견할 수 없었으며, 이는 닭에서 ZFY는 상염색체 또는 Z 염색체에 존재함을 시사한다. SRY의 증폭에서는 성간의 차이가 확인되었으나, 이 유전자가 Z 염색체에 존재하는지 W 염색체에 존재하는지 혹은 상염색체 존재하는지 여부는 연구가 필요하리라 사료된다.