• Journal of the Korean Statistical Society
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• 제36권2호
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• pp.275-284
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• 2007

Confidence intervals for the population proportion in a double sampling scheme subject to false-positive misclassification are considered. The confidence intervals are obtained by applying Agresti and Coull's approach, so-called "adding two-failures and two successes". They are compared in terms of coverage probabilities and expected widths with the Wald interval and the confidence interval given by Boese et al. (2006). The latter one is a test-based confidence interval and is known to have good properties. It is shown that the Agresti and Coull's approach provides a relatively simple but effective confidence interval.

• Journal of the Korean Data and Information Science Society
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• 제22권2호
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• pp.235-243
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• 2011

데이터 마이닝 기법 중에서 가장 많이 이용되고 있는 기법은 연관성 규칙을 탐색하는 것으로, 이 기법은 지지도, 신뢰도, 향상도 등의 연관성 평가 기준을 기반으로 하여 각 항목집합들 간의 관련성을 찾아내는 데 활용되고 있다. 연관성을 평가하기 위한 기준으로 많은 흥미도 측도가 개발되어 있다. 그 중에서도 신뢰도가 가장 많이 활용되고 있으나 신뢰도는 연관성의 방향을 알 수가 없다는 단점을 가지고 있다. 이를 보완하기 위한 측도로 순수 신뢰도가 개발되었으나, 이 또한 양의 신뢰도의 값과 음의 신뢰도의 값이 동일한 경우에는 순수 신뢰도의 값이 같아지므로 이러한 경우에는 순수 신뢰도로는 차이를 알 수 없다. 이에 본 논문에서는 기존의 신뢰도와 순수 신뢰도의 단점을 보완한 연관성 평가기준인 기여 순수 신뢰도를 제안하였다. 또한 예제를 통하여 그 유용성을 알아본 결과, 기여 순수 신뢰도는 그 부호에 의해 연관성 규칙의 방향을 파악할 수 있는 동시에 순수 신뢰도에 의해서는 구분할 수 없는 상황도 해석 가능하게 할 수 있다는 사실을 확인하였다.

• Journal of the Korean Data and Information Science Society
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• 제19권4호
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• pp.1281-1288
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• 2008

The purpose of this study is to consider the efficient methods for introducing the confidence interval. We explain various concepts and approaches about the confidence interval estimation. Computing methods for calculating the efficient confidence interval are suggested.

• 응용통계연구
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• 제22권1호
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• pp.129-140
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• 2009

In this paper, we consider simultaneous confidence intervals for the difference of proportions between two groups taken from multivariate binomial distributions in a nonparametric way. We briefly discuss the construction of simultaneous confidence intervals using the method of adjusting the p-values in multiple tests. The features of bootstrap simultaneous confidence intervals using non-pooled samples are presented. We also compute confidence intervals from the adjusted p-values of multiple tests in the Westfall (1985) style based on a pooled sample. The average coverage probabilities of the bootstrap simultaneous confidence intervals are compared with those of the Bonferroni simultaneous confidence intervals and the Sidak simultaneous confidence intervals. Finally, we give an example that shows how the proposed bootstrap simultaneous confidence intervals can be utilized through data analysis.

• Communications for Statistical Applications and Methods
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• 제16권2호
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• pp.363-372
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• 2009

The difference of two independent binomial proportions is frequently of interest in biomedical research. The interval estimation may be an important tool for the inferential problem. Many confidence intervals have been proposed. They can be classified into the class of exact confidence intervals or the class of approximate confidence intervals. Ore may prefer exact confidence interval s in that they guarantee the minimum coverage probability greater than the nominal confidence level. However, someone, for example Agresti and Coull (1998) claims that "approximation is better than exact." It seems that when sample size is large, the approximate interval is more preferable to the exact interval. However, the choice is not clear when sample, size is small. In this note, an exact confidence and an approximate confidence interval, which were recommended by Santner et al. (2007) and Lee (2006b), respectively, are compared in terms of the coverage probability and the expected length.

• Journal of the Korean Data and Information Science Society
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• 제19권4호
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• pp.1141-1151
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• 2008

Data mining is the process of sorting through large amounts of data and picking out useful information. One of the well-studied problems in data mining is the exploration of association rules. An association rule technique finds the relation among each items in massive volume database. Some interestingness measures have been developed in association rule mining. Interestingness measures are useful in that it shows the causes for pruning uninteresting rules statistically or logically. This paper propose a conditional pure confidence to evaluate association rules and then describe some properties for a proposed measure. The comparative studies with confidence and pure confidence are shown by numerical example. The results show that the conditional pure confidence is better than confidence or pure confidence.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.779-793
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• 2003

In this paper, various methods for finding confidence intervals for common odds ratio $\psi$ of the K 2${\times}$2 tables are reviewed. Also we propose two jackknife confidence intervals and bootstrap confidence intervals for $\psi$. These confidence intervals are compared with the other existing confidence intervals by using Monte Carlo simulation with respect to the average coverage probability.

• Communications for Statistical Applications and Methods
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• 제17권3호
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• pp.309-318
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• 2010

The construction of asymptotic confidence intervals is considered for the difference of binomial proportions in two doubly sampled data subject to false-positive error. The coverage behaviors of several likelihood based confidence intervals and a Bayesian confidence interval are examined. It is shown that a hierarchical Bayesian approach gives a confidence interval with good frequentist properties. Confidence interval based on the Rao score is also shown to have good performance in terms of coverage probability. However, the Wald confidence interval covers true value less often than nominal level.

• 한국멀티미디어학회논문지
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• 제19권9호
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• pp.1647-1658
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• 2016

RGB+D database has been widely used in object recognition, object tracking, robot control, to name a few. While rapid advance of active depth sensing technologies allows for the widespread of indoor RGB+D databases, there are only few outdoor RGB+D databases largely due to an inherent limitation of active depth cameras. In this paper, we propose a novel method used to build outdoor RGB+D databases. Instead of using active depth cameras such as Kinect or LIDAR, we acquire a pair of stereo image using high-resolution stereo camera and then obtain a depth map by applying stereo matching algorithm. To deal with estimation errors that inevitably exist in the depth map obtained from stereo matching methods, we develop an approach that estimates confidence of depth maps based on unsupervised learning. Unlike existing confidence estimation approaches, we explicitly consider a spatial correlation that may exist in the confidence map. Specifically, we focus on refining confidence feature with the assumption that the confidence feature and resultant confidence map are smoothly-varying in spatial domain and are highly correlated to each other. Experimental result shows that the proposed method outperforms existing confidence measure based approaches in various benchmark dataset.

• 응용통계연구
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• 제19권1호
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• pp.171-181
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• 2006

모비율 차이의 구간 추정에서 표준으로 인식되고 있는 Wald 신뢰구간은 모비율 구간 추정과 마찬가지로 포함확률의 근사성에서 문제가 있다는 것이 알려져 있다. 이에 대한 대안으로 모비율 차이의 신뢰구간에 대한 많은 연구가 있어 왔으나 대부분의 신뢰구간은 매우 복잡한 과정을 통해 얻어지게 되어 있어 실용성에 대한 문제가 제기될 수 있다. 이와 비교하여 Agresti와 Caffo(2000)에 의해 제시된 신뢰구간은 매우 간편한 식에 의해 구할 수 있어 이해하기 쉽고 포함확률과 포함확률의 평균절대오차에 있어 다른 복잡한 신뢰 구간과 필적할 수 있다. 그러나 Agresti-Caffo 신뢰 구간은 포함확률이 명목 신뢰수준을 상회하는 보수적인 구간으로 알려져 있다. 본 논문에서는 이승천(2005)에서 이항비율의 신뢰구간을 구하기 위해 사용된 가중 Polya 사후분포를 이용하여 두 모비율 차이의 신뢰구간을 구하였다. 이렇게 구하여진 신뢰구간은 간편성은 물론 Agresti-Caffo 신뢰구간의 보수성을 개선하였다.