The Experts below are selected from a list of 3594 Experts worldwide ranked by ideXlab platform
Dewi Rahardja - One of the best experts on this subject based on the ideXlab platform.
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maximum likelihood estimation for the Proportion difference of two sample Binomial data subject to one type of misclassification
Journal of Statistics and Management Systems, 2019Co-Authors: Dewi Rahardja, Han Wu, Zhiwei Zhang, Andrew D TiedtAbstract: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...
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Bayesian Credible Sets for Odds Ratio of Two Binomial Proportions Using Double Sampling with False-Positive Misclassification
American Journal of Mathematical and Management Sciences, 2016Co-Authors: Dewi RahardjaAbstract: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.
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Confidence intervals for the odds ratio of two independent Binomial Proportions using data with one type of misclassification
Journal of Statistics and Management Systems, 2016Co-Authors: Dewi Rahardja, Han Wu, Shuguang HuangAbstract: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.
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maximum likelihood estimation of a Binomial Proportion using one sample misclassified binary data
Statistica Neerlandica, 2015Co-Authors: Dewi Rahardja, Ying YangAbstract:type="main"> In this article, we construct two likelihood-based confidence intervals (CIs) for a Binomial Proportion parameter using a double-sampling scheme with misclassified binary data. We utilize an easy-to-implement closed-form algorithm to obtain maximum likelihood estimators of the model parameters by maximizing the full-likelihood function. The two CIs are a naive Wald interval and a modified Wald interval. Using simulations, we assess and compare the coverage probabilities and average widths of our two CIs. Finally, we conclude that the modified Wald interval, unlike the naive Wald interval, produces close-to-nominal CIs under various simulations and, thus, is preferred in practice. Utilizing the expressions derived, we also illustrate our two CIs for a Binomial Proportion parameter using real-data example.
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Maximum Likelihood Estimation for a Binomial Parameter Using Double Sampling with one Type of Misclassification
American Journal of Mathematical and Management Sciences, 2015Co-Authors: Dewi Rahardja, Ying Yang, Yongming QuAbstract:SYNOPTIC ABSTRACTA Maximum Likelihood Estimator (MLE) approach is considered for the estimation of a Binomial Proportion parameter in doubly sampled data subject to false positive misclassification. We assume that an inexpensive, error-prone device is used on a large main study and an expensive, error-free device is utilized on a smaller substudy. This double sample allows identifiability of all unknown parameters, because by incorporating additional information (data) via double sampling, the dimension of sufficient statistics is greater than or equal to the numbers of parameters; hence, the model becomes identifiable. Additionally, we derive two confidence intervals (CIs): a naive Wald CI and a modified Wald CI, and we compare the performance of these two CIs in terms of coverage probability and average length, via a Monte Carlo simulation. We then apply the two newly derived estimator and confidence intervals to a real data problem.
Anirban Dasgupta - One of the best experts on this subject based on the ideXlab platform.
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confidence intervals for a Binomial Proportion and asymptotic expansions
Annals of Statistics, 2002Co-Authors: Lawrence D Brown, Anirban DasguptaAbstract: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).
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interval estimation for a Binomial Proportion
Statistical Science, 2001Co-Authors: Lawrence D Brown, Anirban DasguptaAbstract: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 - One of the best experts on this subject based on the ideXlab platform.
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modified clopper pearson confidence interval for Binomial Proportion
Journal of Statistical Theory and Applications, 2014Co-Authors: Desale Habtzghi, Chand K MidhaAbstract: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.
Mans Thulin - One of the best experts on this subject based on the ideXlab platform.
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approximate bayesianity of frequentist confidence intervals for a Binomial Proportion
The American Statistician, 2017Co-Authors: Mans Thulin, Rolf LarssonAbstract:ABSTRACTThe well-known Wilson and Agresti–Coull confidence intervals for a Binomial Proportion p are centered around a Bayesian estimator. Using this as a starting point, similarities between frequentist confidence intervals for Proportions and Bayesian credible intervals based on low-informative priors are studied using asymptotic expansions. A Bayesian motivation for a large class of frequentist confidence intervals is provided. It is shown that the likelihood ratio interval for p approximates a Bayesian credible interval based on Kerman’s neutral noninformative conjugate prior up to O(n− 1) in the confidence bounds. For the significance level α ≲ 0.317, the Bayesian interval based on the Jeffreys’ prior is then shown to be a compromise between the likelihood ratio and Wilson intervals. Supplementary materials for this article are available online.
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the cost of using exact confidence intervals for a Binomial Proportion
Electronic Journal of Statistics, 2014Co-Authors: Mans ThulinAbstract:When computing a confidence interval for a Binomial Proportion p one must choose between using an exact interval, which has a coverage probability of at least 1 a for all values of p, and a shorter approximate interval, which may have lower coverage for some p but that on average has coverage equal to 1 a. We investigate the cost of using the exact one and two-sided Clopper-Pearson confidence intervals rat her than shorter approximate intervals, first in terms of increased expected length and then in terms of the increase in sample size required to obtain a desired expected length. Using asymptotic expansions, we also give a closed-form formula for determining the sample size for the exact Clopper-Pearson methods. For two-sided intervals, our investigation reveals an interesting connection between the frequentist Clopper-Pearson interval and Bayesian intervals based on noninformative priors.
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Coverage‐adjusted Confidence Intervals for a Binomial Proportion
Scandinavian Journal of Statistics, 2013Co-Authors: Mans ThulinAbstract:type="main" xml:id="sjos12021-abs-0001"> We consider the classic problem of interval estimation of a Proportion p based on Binomial sampling. The ‘exact’ Clopper–Pearson confidence interval for p is known to be unnecessarily conservative. We propose coverage adjustments of the Clopper–Pearson interval that incorporate prior or posterior beliefs into the interval. Using heatmap-type plots for comparing confidence intervals, we show that the coverage-adjusted intervals have satisfying coverage and shorter expected lengths than competing intervals found in the literature.
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coverage adjusted confidence intervals for a Binomial Proportion
arXiv: Methodology, 2012Co-Authors: Mans ThulinAbstract:We consider the classic problem of interval estimation of a Proportion $p$ based on Binomial sampling. The "exact" Clopper-Pearson confidence interval for $p$ is known to be unnecessarily conservative. We propose coverage-adjustments of the Clopper-Pearson interval using prior and posterior distributions of $p$. The adjusted intervals have improved coverage and are often shorter than competing intervals found in the literature. Using new heatmap-type plots for comparing confidence intervals, we find that the coverage-adjusted intervals are particularly suitable for $p$ close to 0 or 1.
Lawrence D Brown - One of the best experts on this subject based on the ideXlab platform.
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confidence intervals for a Binomial Proportion and asymptotic expansions
Annals of Statistics, 2002Co-Authors: Lawrence D Brown, Anirban DasguptaAbstract: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).
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interval estimation for a Binomial Proportion
Statistical Science, 2001Co-Authors: Lawrence D Brown, Anirban DasguptaAbstract: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.