• Title, Summary, Keyword: Confidence

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AN IMPROVED CONFIDENCE INTERVAL FOR THE POPULATION PROPORTION IN A DOUBLE SAMPLING SCHEME SUBJECT TO FALSE-POSITIVE MISCLASSIFICATION

  • Lee, Seung-Chun
    • Journal of the Korean Statistical Society
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    • v.36 no.2
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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.

The proposition of attributably pure confidence in association rule mining (연관 규칙 마이닝에서 기여 순수 신뢰도의 제안)

  • Park, Hee-Chang
    • Journal of the Korean Data and Information Science Society
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    • v.22 no.2
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    • pp.235-243
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    • 2011
  • The most widely used data mining technique is to explore association rules. This technique has been used to find the relationship between each set of items based on the association thresholds such as support, confidence, lift, etc. There are many interestingness measures as the criteria for evaluating association rules. Among them, confidence is the most frequently used, but it has the drawback that it can not determine the direction of the association. The net confidence measure was developed to compensate for this drawback, but it is useless in the case that the value of positive confidence is the same as that of negative confidence. This paper propose a attributably pure confidence to evaluate association rules and then describe some properties for a proposed measure. The comparative studies with confidence, net confidence, and attributably pure confidence are shown by numerical example. The results show that the attributably pure confidence is better than confidence or net confidence.

Constructing Simultaneous Confidence Intervals for the Difference of Proportions from Multivariate Binomial Distributions

  • Jeong, Hyeong-Chul;Kim, Dae-Hak
    • The Korean Journal of Applied Statistics
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    • v.22 no.1
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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.

Choosing between the Exact and the Approximate Confidence Intervals: For the Difference of Two Independent Binomial Proportions

  • Lee, Seung-Chun
    • Communications for Statistical Applications and Methods
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    • v.16 no.2
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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.

The Proposition of Conditionally Pure Confidence in Association Rule Mining

  • Park, Hee-Chang
    • Journal of the Korean Data and Information Science Society
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    • v.19 no.4
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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.

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On the Efficient Teaching Method of Confidence Interval in College Education

  • Kim, Yeung-Hoon;Ko, Jeong-Hwan
    • Journal of the Korean Data and Information Science Society
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    • v.19 no.4
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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.

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A Comparison Study for the Confidence Intervals of the Common Odds Ratio in the Stratified 2 X 2 Tables Using the Average Coverage Probability

  • Kwak, Min Jung;Jeong, Hyeong Chul
    • Communications for Statistical Applications and Methods
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    • v.10 no.3
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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.

Confidence Intervals for the Difference of Binomial Proportions in Two Doubly Sampled Data

  • Lee, Seung-Chun
    • Communications for Statistical Applications and Methods
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    • v.17 no.3
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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.

Confidence Intervals for a Proportion in Finite Population Sampling

  • Lee, Seung-Chun
    • Communications for Statistical Applications and Methods
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    • v.16 no.3
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    • pp.501-509
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    • 2009
  • Recently the interval estimation of binomial proportions is revisited in various literatures. This is mainly due to the erratic behavior of the coverage probability of the well-known Wald confidence interval. Various alternatives have been proposed. Among them, the Agresti-Coull confidence interval, the Wilson confidence interval and the Bayes confidence interval resulting from the noninformative Jefferys prior were recommended by Brown et al. (2001). However, unlike the binomial distribution case, little is known about the properties of the confidence intervals in finite population sampling. In this note, the property of confidence intervals is investigated in anile population sampling.

Confidence Measure of Depth Map for Outdoor RGB+D Database (야외 RGB+D 데이터베이스 구축을 위한 깊이 영상 신뢰도 측정 기법)

  • Park, Jaekwang;Kim, Sunok;Sohn, Kwanghoon;Min, Dongbo
    • Journal of Korea Multimedia Society
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    • v.19 no.9
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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.