• 대한수학회지
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• 제53권6호
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• pp.1275-1292
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• 2016

The Marcinkiewicz-Zygmund strong law of large numbers for conditionally independent and conditionally identically distributed random variables is an existing, but merely qualitative result. In this paper, for the more general cases where the conditional order of moment belongs to (0, ${\infty}$) instead of (0, 2), we derive results on convergence rates which are quantitative ones in the sense that they tell us how fast convergence is obtained. Furthermore, some conditional probability inequalities are of independent interest.

• Communications for Statistical Applications and Methods
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• 제22권6호
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• pp.599-604
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• 2015

Kendall's tau statistic has been applied to test an association of bivariate random variables. However, incomplete bivariate data with a truncation and a censoring results in incomparable or unorderable pairs. With such a partial information, Tsai (1990) suggested a conditional tau statistic and a test procedure for a quasi independence that was extended to more diverse cases such as double truncation and a semi-competing risk data. In this paper, we also employed a conditional tau statistic to estimate an association of bivariate interval censored data. The suggested method shows a better result in simulation studies than Betensky and Finkelstein's multiple imputation method except a case in cases with strong associations. The association of incubation time and infection time from an AIDS cohort study is estimated as a real data example.

• 대한수학회지
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• 제54권2호
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• pp.441-460
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• 2017

In this paper, we establish some conditional versions of the first part of the Borel-Cantelli lemma. As its applications, we study strong limit results of $\mathfrak{F}$-independent random variables sequences, the convergence of sums of $\mathfrak{F}$-independent random variables and the conditional version of strong limit results of the concomitants of order statistics.

• Journal of Computing Science and Engineering
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• 제9권4호
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• pp.204-213
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• 2015

Under the conditional independence assumption among local features, the Naive Bayes Nearest Neighbor (NBNN) classifier has been recently proposed and performs classification without any training or quantization phases. While the original NBNN shows high classification accuracy without adopting an explicit training phase, the conditional independence among local features is against the compositionality of objects indicating that different, but related parts of an object appear together. As a result, the assumption of the conditional independence weakens the accuracy of classification techniques based on NBNN. In this work, we look into this issue, and propose a novel Bayesian network for an NBNN based classification to consider the conditional dependence among features. To achieve our goal, we extract a high-level feature and its corresponding, multiple low-level features for each image patch. We then represent them based on a simple, two-level layered Bayesian network, and design its classification function considering our Bayesian network. To achieve low memory requirement and fast query-time performance, we further optimize our representation and classification function, named relation-based Bayesian network, by considering and representing the relationship between a high-level feature and its low-level features into a compact relation vector, whose dimensionality is the same as the number of low-level features, e.g., four elements in our tests. We have demonstrated the benefits of our method over the original NBNN and its recent improvement, and local NBNN in two different benchmarks. Our method shows improved accuracy, up to 27% against the tested methods. This high accuracy is mainly due to consideration of the conditional dependences between high-level and its corresponding low-level features.

• 대한수학회지
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• 제51권3호
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• pp.609-633
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• 2014

From the ordinary notion of uniformly strong mixing for a sequence of random variables, a new concept called conditionally uniformly strong mixing is proposed and the relation between uniformly strong mixing and conditionally uniformly strong mixing is answered by examples, that is, uniformly strong mixing neither implies nor is implied by conditionally uniformly strong mixing. A couple of equivalent definitions and some of basic properties of conditionally uniformly strong mixing random variables are derived, and several conditional covariance inequalities are obtained. By means of these properties and conditional covariance inequalities, a conditional central limit theorem stated in terms of conditional characteristic functions is established, which is a conditional version of the earlier result under the non-conditional case.

• Journal of the Korean Data and Information Science Society
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• 제25권4호
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• pp.881-891
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• 2014

그래프모형(graphical model)은 확률 변수들간의 조건부 독립성(conditional independence)을 시각적인 네트워크형태로 표현할 수 있기 때문에, 정보학 (bioinformatics)이나 사회관계망 (social network) 등 수많은 변수들이 서로 연결되어 있는 복잡한 확률 시스템에 대한 직관적인 도구로 활용될 수 있다. 그래프 LASSO (graphical least absolute shrinkage and selection operator)는 고차원의 자료에 대한 가우스 그래프 모형 (Gaussian graphical model)의 추정에서 과대적합 (overfitting)을 방지하는데에 효과적인 것으로 알려진 방법이다. 본 논문에서는 그래프 LASSO 추정에서 매우 중요한 문제인 모형선택에 대하여 고려한다. 특히 여러가지 모형선택기준을 모의실험을 통해 비교하며 실제 금융 자료를 분석한다.

• 응용통계연구
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• 제11권2호
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• pp.257-268
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• 1998

역학연구에서 두 수준을 갖는 위험인자 X와 이항 반응변수 Y의 관계에 관심을 갖는 경우가 많다. 이 때 두 변수의 상관관계에 영향을 미칠 수 있는 인자 Z의 값을 제어함에 따라 X와 Y의 상관관계가 여전히 존재하는지를, 즉 X와 Y의 조건부 독립성을 검정할 필요가 있다 관측값의 수가 많지 않을 때, X와 Y의 조건부 독립성 검정을 위해 Mantel-Haenszel 검 정 이 널리 사용되고 있다. 하지 만 X와 Y의 상관관계가 Z의 수준에 따라 그 방향까지 변할 경우 이 검정은 낮은 검정력을 갖는다. 본 연구에서는 이 경우에 높은 검정력을 갖는 대안 검정통계량을 제안한다. 대안 검정통계량의 분포에 대해 알아보고 모의실험을 통해 Mantel-Haenszel 검정과 비교해 본다.

• 대한수학회지
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• 제51권1호
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• pp.1-15
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• 2014

A conditional version of the classical central limit theorem is derived rigorously by using conditional characteristic functions, and a more general version of conditional central limit theorem for the case of conditionally independent but not necessarily conditionally identically distributed random variables is established. These are done anticipating that the field of conditional limit theory will prove to be of significant applicability.

• 한국수학교육학회지시리즈C:초등수학교육
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• 제14권1호
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• pp.69-79
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• 2011

본 연구의 목적은 확률을 학습하지 않은 3, 4, 5학년 학생들의 확률 개념에 대한 이해 수준을 살펴보고, 확률 학습에 대한 가능성을 탐색하는 것이다. 이를 위해 3, 4, 5학년 학생을 대상으로 지필검사를 통한 조사 연구를 실시하였고, 선행연구를 토대로 한 확률 이해 분석의 틀을 분석기준으로 삼았다. 본 연구의 결과 학생들의 확률 개념 평균 이해 수준은 표본공간에서 가장 높게 나타났고 사건의 확률, 공평성, 확률 비교 순이었으며, 특히 표본공간에 대해 가장 높은 수준을 나타냈고 이러한 결과는 3, 4, 5학년의 공통적인 현상이었다. 반면 학생들의 독립성에 대한 이해 수준은 낮은 편이었고 학년 간에 유의한 수준 차이가 없었으며, 조건부 확률에 대한 이해는 가장 낮았다.

• Communications for Statistical Applications and Methods
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• 제26권5호
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• pp.507-517
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• 2019

Statistical matching is a method of combining multiple sources of data that are extracted or surveyed from the same population. It can be used in situation when variables of interest are not jointly observed. It is a low-cost way to expect high-effects in terms of being able to create synthetic data using existing sources. In this paper, we propose the several statistical micro matching methods using a multinomial logistic regression model when all variables of interest are categorical or categorized ones, which is common in sample survey. Under conditional independence assumption (CIA), a mixed statistical matching method, which is useful when auxiliary information is not available, is proposed. We also propose a statistical matching method with auxiliary information that reduces the bias of the conventional matching methods suggested under CIA. Through a simulation study, proposed micro matching methods and conventional ones are compared. Simulation study shows that suggested matching methods outperform the existing ones especially when CIA does not hold.