• Title/Summary/Keyword: confidence probability

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A Comparison of Confidence Intervals for the Difference of Proportions (모비율 차이의 신뢰구간들에 대한 비교연구)

  • 정형철;전명식;김대학
    • The Korean Journal of Applied Statistics
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    • v.16 no.2
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    • pp.377-393
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    • 2003
  • Several confidence interval estimates for the difference of two binomial proportions were introduced. Bootstrap confidence interval is also suggested. We examined the over estimation property of approximate intervals and under estimation trend of exact intervals for the difference of proportions. We compared these confidence intervals based on the average coverage probability, expected width and skewness measure. Particularly actual coverage probability were calculated by using the prior distribution of parameters. Monte Carlo simulation for small sample size is conducted. Some interesting contour plots of average coverage probability and marginal plots for several interval estimates are presented.

Formal Trust Assessment with Confidence Probability

  • Kutay, Mahir;Ercan, Tuncay
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.2
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    • pp.830-842
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    • 2015
  • Trust and trustworthiness of web services and organizations is calculated as scalar values. But there is still a certain level of risk for the overall reliability of this value. In this article, we focus on calculating trust values as intervals between upper and lower bounds based on predefined confidence values through an additional confidence probability. This will give us a more realistic approach to the trust assessments between individuals and organizations. We also developed a web-based software tool, TAST (Trust Assessment Software Tool) that collects the web services' evaluation of different customer groups for similar organizations through the user interface and calculates the trust intervals for predefined and previously selected confidence values. Our model uses a weighted calculation of mean and variances of customer groups in specific periods and analyses the total and incremental trust of different customer groups.

On the actual coverage probability of hypergeometric parameter (초기하분포의 모수에 대한 신뢰구간추정)

  • Kim, Dae-Hak
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.6
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    • pp.1109-1115
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    • 2010
  • In this paper, exact confidence interval of hyper-geometric parameter, that is the probability of success p in the population is discussed. Usually, binomial distribution is a well known discrete distribution with abundant usage. Hypergeometric distribution frequently replaces a binomial distribution when it is desirable to make allowance for the finiteness of the population size. For example, an application of the hypergeometric distribution arises in describing a probability model for the number of children attacked by an infectious disease, when a fixed number of them are exposed to it. Exact confidence interval estimation of hypergeometric parameter is reviewed. We consider the performance of exact confidence interval estimates of hypergeometric parameter in terms of actual coverage probability by small sample Monte Carlo simulation.

Logit Confidence Intervals Using Pseudo-Bayes Estimators for the Common Odds Ratio in 2 X 2 X K Contingency Tables

  • Kim, Donguk;Chun, Eunhee
    • Communications for Statistical Applications and Methods
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    • v.10 no.2
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    • pp.479-496
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    • 2003
  • We investigate logit confidence intervals for the odds ratio based on the delta method. These intervals are constructed using pseudo-Bayes estimators. The Gart method and Agresti method smooth the observed counts toward the model of equiprobability and independence, respectively. We obtain better coverage probability by smoothing the observed counts toward the pseudo-Bayes estimators in 2$\times$2 table. We also improve legit confidence intervals in 2$\times$2$\times$K tables by generalizing these ideas. Utilizing pseudo-Bayes estimators, we obtain better coverage probability by smoothing the observed counts toward the conditional independence model, no three-factor interaction model and saturated model in 2$\times$2$\times$K tables.

Utterance Verification Using Search Confusion Rate and Its N-Best Approach

  • Kim, Kyu-Hong;Kim, Hoi-Rin;Hahn, Min-Soo
    • ETRI Journal
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    • v.27 no.4
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    • pp.461-464
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    • 2005
  • Recently, a variety of confidence measures for utterance verification has been studied to improve speech recognition performance by rejecting out-of-vocabulary inputs. Most of the conventional confidence measures for utterance verification are based primarily on hypothesis testing or an approximated posterior probability, and their performances depend on the robustness of an alternative hypothesis or the prior probability. We introduce a novel confidence measure called a search confusion rate (SCR), which does not require an alternative hypothesis or the approximation of posterior probability. Our confusion-based approach shows better performance in additive noise-corrupted speech as well as in clean speech.

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On Confidence Interval for the Probability of Success

  • Sang-Joon Lee;M. T. Longnecker;Woochul Kim
    • Communications for Statistical Applications and Methods
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    • v.3 no.3
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    • pp.263-269
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    • 1996
  • The somplest approximate confidence interval for the probability of success is the one based on the normal approximation to the binomial distribution, It is widely used in the introductory teaching, and various guidelines for its use with "large" sample have appeared in the literature. This paper suggests a guideline when to use it as an approximation to the exact confidence interval, and comparisons with existing guidelines are provided. provided.

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A Lower Confidence Bound on the Probability of a Correct Selection of the t Best Populations

  • Jeong, Gyu-Jin;Kim, Woo-Chul;Jeon, Jong-Woo
    • Journal of the Korean Statistical Society
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    • v.18 no.1
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    • pp.26-37
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    • 1989
  • When we select the t best out of k populations in the indifference zone formulation, a lower confidence bound on the probability of a correct selection is derived for families with monotone likelihood ratio. The result is applied to the normal means problem when the variance is common, and to the normal variances problem. Tables to implement the confidence bound for the normal variances problem are provided.

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Evaluation of Probability Rainfalls Estimated from Non-Stationary Rainfall Frequency Analysis (비정상성 강우빈도해석법에 의한 확률강우량의 평가)

  • Lee, Chang-Hwan;Ahn, Jae-Hyun;Kim, Tae-Woong
    • Journal of Korea Water Resources Association
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    • v.43 no.2
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    • pp.187-199
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    • 2010
  • This study evaluated applicability and confidence of probability rainfalls estimated by the non-stationary rainfall frequency analysis which was recently developed. Using rainfall data at 4 sites which have an obvious increasing trend in observations, we estimated 3 type probability rainfalls; probability rainfalls from stationary rainfall frequency analysis using data from 1973-1997, probability rainfalls from stationary rainfall frequency analysis using data from 1973-2006, probability rainfalls from non-stationary rainfall frequency analysis assuming that the current year is 1997 and the target year is 2006. Based on the comparison of residuals from 3 probability rainfalls, the non-stationary rainfall frequency analysis provided more effective and well-directed estimates of probability rainfalls in the target year. Using Bootstrap resampling, this study also evaluated the parameter estimation methods for the non-stationary rainfall frequency analysis based on confidence intervals. The confidence interval length estimated by the maximum likelihood estimation (MLE) is narrower than the probability weighted moments (PWM). The results indicated that MLE provides more proper confidence than PWM for non-stationary probability rainfalls.

On Prediction Intervals for Binomial Data (이항자료에 대한 예측구간)

  • Ryu, Jea-Bok
    • The Korean Journal of Applied Statistics
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    • v.26 no.6
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    • pp.943-952
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    • 2013
  • Wald, Agresti-Coull, Jeffreys, and Bayes-Laplace methods are commonly used for confidence interval of binomial proportion are applied for prediction intervals. We used coverage probability, mean coverage probability, root mean squared error, and mean expected width for numerical comparisons. From the comparisons, we found that Wald is not proper as for confidence interval and Agresti-Coull is too conservative to differ from confidence interval. However, Jeffrey and Bayes-Laplace are good for prediction interval and Jeffrey is especially desirable as for confidence interval.

On prediction intervals for binomial data (이항자료에 대한 예측구간)

  • Ryu, Jea-Bok
    • The Korean Journal of Applied Statistics
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    • v.34 no.4
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    • pp.579-588
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    • 2021
  • Wald, Agresti-Coull, Jeffreys, and Bayes-Laplace methods are commonly used for confidence interval of binomial proportion are applied for prediction intervals. We used coverage probability, mean coverage probability, root mean squared error, and mean expected width for numerical comparisons. From the comparisons, we found that Wald is not proper as for confidence interval and Agresti-Coull is too conservative to differ from confidence interval. However, Jeffrey and Bayes-Laplace are good for prediction interval and Jeffrey is especially desirable as for confidence interval.