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Binomial Proportion

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Dewi Rahardja – 1st expert on this subject based on the ideXlab platform

  • maximum likelihood estimation for the Proportion difference of two sample Binomial data subject to one type of misclassification
    Journal of Statistics and Management Systems, 2019
    Co-Authors: Dewi Rahardja, Han Wu, Zhiwei Zhang, Andrew D Tiedt


    AbstractIn this manuscript, we derived three likelihood-based interval estimation methods using a closed-form algorithm for the difference of two independent Binomial Proportion parameters with one…

  • Bayesian Credible Sets for Odds Ratio of Two Binomial Proportions Using Double Sampling with False-Positive Misclassification
    American Journal of Mathematical and Management Sciences, 2016
    Co-Authors: Dewi Rahardja


    SYNOPTIC ABSTRACTA Bayesian approach is considered for a point and interval estimation for odds ratios of two Binomial Proportion parameters using double-sampling data subject to false-positive misclassification. For such data, we derive an easy-to-implement, closed-form algorithm for drawing from the posterior distributions. Application of this algorithm is illustrated using data collected about student laboratory grades from a course.

  • Confidence intervals for the odds ratio of two independent Binomial Proportions using data with one type of misclassification
    Journal of Statistics and Management Systems, 2016
    Co-Authors: Dewi Rahardja, Han Wu, Shuguang Huang


    AbstractWe derive a confidence interval for the odd ratio of two independent Binomial Proportion parameters with one type of misclassification using an easy-to-implement closed- form algorithm based on the Maximum Likelihood Estimator (MLE) and its asymptotic variance. The model identifiability is obtained via doubly sampled data. For illustration, we apply our methods to a sudden infant death syndrome (SIDS) dataset.

Anirban Dasgupta – 2nd expert on this subject based on the ideXlab platform

  • confidence intervals for a Binomial Proportion and asymptotic expansions
    Annals of Statistics, 2002
    Co-Authors: Lawrence D Brown, Anirban Dasgupta


    p ± zα/2n −1/2 ( ˆ p(1 −ˆ p)) 1/2 is currently in near universal use. We first show that the coverage properties of the Wald interval are persistently poor and defy virtually all conventional wisdom. We then proceed to a theoretical comparison of the standard interval and four additional alternative intervals by asymptotic expansions of their coverage probabilities and expected lengths. The four additional interval methods we study in detail are the score-test interval (Wilson), the likelihood-ratio-test interval, a Jeffreys prior Bayesian interval and an interval suggested by Agresti and Coull. The asymptotic expansions for coverage show that the first three of these alternative methods have coverages that fluctuate about the nominal value, while the Agresti– Coull interval has a somewhat larger and more nearly conservative coverage function. For the five interval methods we also investigate asymptotically their average coverage relative to distributions for p supported within (0, 1). In terms of expected length, asymptotic expansions show that the Agresti– Coull interval is always the longest of these. The remaining three are rather comparable and are shorter than the Wald interval except for p near 0 or 1. These analytical calculations support and complement the findings and the recommendations in Brown, Cai and DasGupta (Statist. Sci. (2001) 16 101–133).

  • interval estimation for a Binomial Proportion
    Statistical Science, 2001
    Co-Authors: Lawrence D Brown, Anirban Dasgupta


    We revisit the problem of interval estimation of a Binomial Proportion. The erratic behavior of the coverage probability of the stan- d ardWaldconfid ence interval has previously been remarkedon in the literature (Blyth andStill, Agresti andCoull, Santner andothers). We begin by showing that the chaotic coverage properties of the Waldinter- val are far more persistent than is appreciated. Furthermore, common textbook prescriptions regarding its safety are misleading and defective in several respects andcannot be trusted . This leads us to consideration of alternative intervals. A number of natural alternatives are presented, each with its motivation and con- text. Each interval is examinedfor its coverage probability andits length. Basedon this analysis, we recommendthe Wilson interval or the equal- tailedJeffreys prior interval for small n andthe interval suggestedin Agresti andCoull for larger n. We also provide an additional frequentist justification for use of the Jeffreys interval.

Chand K Midha – 3rd expert on this subject based on the ideXlab platform

  • modified clopper pearson confidence interval for Binomial Proportion
    Journal of Statistical Theory and Applications, 2014
    Co-Authors: Desale Habtzghi, Chand K Midha


    We introduce expected coverage probability as a measure for constructing confidence intervals for the Binomial Proportion, p. We propose a model based confidence interval for p using the expected coverage probabilities of the Clopper-Pearson interval. The method provides intervals comparable or better than the alternative intervals, such as the Wilson, Agresti-Coull and Jeffreys intervals.