The Experts below are selected from a list of 754707 Experts worldwide ranked by ideXlab platform
Douglas G Bonett - One of the best experts on this subject based on the ideXlab platform.
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Confidence Interval for a coefficient of quartile variation
Computational Statistics & Data Analysis, 2006Co-Authors: Douglas G BonettAbstract:An approximate Confidence Interval is proposed for a robust measure of relative dispersion-the coefficient of quartile variation. The proposed method provides an alternative to Interval estimates for other measures of relative dispersion.
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Confidence Interval for a coefficient of dispersion in nonnormal distributions
Biometrical Journal, 2006Co-Authors: Douglas G Bonett, Edith SeierAbstract:Summary A new Confidence Interval for the coefficient of dispersion (mean absolute deviation from the median divided by median) is proposed and is shown to perform better than the BCa bootstrap Confidence Interval.
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approximate Confidence Interval for standard deviation of nonnormal distributions
Computational Statistics & Data Analysis, 2006Co-Authors: Douglas G BonettAbstract:The exact Confidence Interval for @s is hypersensitive to minor violations of the normality assumption and its performance does not improve with increasing sample size. An approximate Confidence Interval for @s is proposed and is shown to be nearly exact under normality with excellent small-sample properties under moderate nonnormality. The small-sample performance of the proposed Interval may be further improved using prior kurtosis information. A sample size planning formula is given.
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an improved Confidence Interval for a linear function of binomial proportions
Computational Statistics & Data Analysis, 2004Co-Authors: Robert M Price, Douglas G BonettAbstract:We propose a simple adjustment to a Wald Confidence Interval to estimate a linear function of binomial proportions. This method is an extension to the adjusted Wald Confidence Intervals for proportions and their differences that have recently been proposed.
Allan Donner - One of the best experts on this subject based on the ideXlab platform.
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a comparison of Confidence Interval methods for the intraclass correlation coefficient in community based cluster randomization trials with a binary outcome
Clinical Trials, 2016Co-Authors: Melissa C Braschel, Ivana Svec, Gerarda Darlington, Allan DonnerAbstract:Background:Many investigators rely on previously published point estimates of the intraclass correlation coefficient rather than on their associated Confidence Intervals to determine the required size of a newly planned cluster randomized trial. Although Confidence Interval methods for the intraclass correlation coefficient that can be applied to community-based trials have been developed for a continuous outcome variable, fewer methods exist for a binary outcome variable. The aim of this study is to evaluate Confidence Interval methods for the intraclass correlation coefficient applied to binary outcomes in community intervention trials enrolling a small number of large clusters. Existing methods for Confidence Interval construction are examined and compared to a new ad hoc approach based on dividing clusters into a large number of smaller sub-clusters and subsequently applying existing methods to the resulting data.Methods:Monte Carlo simulation is used to assess the width and coverage of Confidence int...
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Confidence Interval construction for a difference between two dependent intraclass correlation coefficients
Statistics in Medicine, 2009Co-Authors: Chinthanie F Ramasundarahettige, Allan Donner, Guangyong ZouAbstract:Inferences for the difference between two dependent intraclass correlation coefficients (ICCs) may arise in studies in which a sample of subjects are each assessed several times with a new device and a standard. The ICC estimates for the two devices may then be compared using a test of significance. However, a Confidence Interval for a difference between two ICCs is more informative since it combines point estimation and hypothesis testing into a single inference statement. We propose a procedure that uses Confidence limits for a single ICC to recover variance estimates needed to set Confidence limits for the difference. An advantage of this approach is that it provides a Confidence Interval that reflects the underlying sampling distribution. Simulation results show that this method performs very well in terms of overall coverage percentage and tail errors. Two data sets are used to illustrate this procedure.
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Confidence Interval construction for effect measures arising from cluster randomization trials
Journal of Clinical Epidemiology, 1993Co-Authors: Allan Donner, Neil KlarAbstract:Methods of Confidence Interval construction are provided for summary measures of treatment effect arising from designs randomizing clusters to one of two treatment groups. Three basic designs are considered for the case of continuous and dichotomous variables: completely randomized, pair-matched and stratified.
Masayo Yoshimori Hirose - One of the best experts on this subject based on the ideXlab platform.
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non area specific adjustment factor for second order efficient empirical bayes Confidence Interval
Computational Statistics & Data Analysis, 2017Co-Authors: Masayo Yoshimori HiroseAbstract:An empirical Bayes Confidence Interval has high user demand in many applications. In particular, the second-order empirical Bayes Confidence Interval, the coverage error of which is of the third order for a large number of areas, m, is widely used in small area estimation when the sample size within each area is not large enough to make reliable direct estimates according to a design-based approach. Yoshimori and Lahiri (2014a) proposed a new type of Confidence Interval, called the second-order efficient empirical Bayes Confidence Interval, with a length less than that of the direct Confidence estimated according to the design-based approach. However, this Interval still has some disadvantages: (i) it is hard to use when at least one leverage value is high; (ii) many iterations tend to be required to obtain the estimators of one global model variance parameter as the number of areas, m, increases, due to the area-specific adjustment factor. To prevent such issues, this study proposes a more efficient Confidence Interval to allow for high leverage and reduce the number of iterations for large m. To achieve this purpose, we theoretically obtained a non-area-specific adjustment factor and the measure of uncertainty of the empirical Bayes estimator, which consist of empirical Bayes Confidence Interval, maintaining the existing desired properties. Moreover, we present three simulation results and real data analysis to show overall superiority of our Confidence Interval method over the other methods, including the one proposed in Yoshimori and Lahiri (2014a).
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non area specific adjustment factor for second order efficient empirical bayes Confidence Interval
arXiv: Statistics Theory, 2016Co-Authors: Masayo Yoshimori HiroseAbstract:An empirical Bayes Confidence Interval has high user demand in many applications. In particular, the second-order empirical Bayes Confidence Interval, the coverage error of which is of the third order for large number of areas, is widely used in small area estimation when the sample size within each area is not large enough to make reliable direct estimates based on a design-based approach. Yoshimori and Lahiri (2014a) proposed a new type of Confidence Interval, called the second-order efficient empirical Bayes Confidence Interval, whose length is less than that of the direct Confidence Interval based on the design-based approach. However, this Interval still has some disadvantages: (i) it is hard to use when at least one leverage value is high; (ii) many iterations tend to be required to obtain the estimators of one global model variance parameter as the number of areas getting larger, due to the area-specific adjustment factor. To prevent such issues, this paper proposes, as never done before, a more efficient Confidence Interval to allow for high leverage and reduce the number of iterations for large number of areas, by adopting a non-area-specific adjustment factor, maintaining the existing desired properties. We also reveal the relationship between the general adjustment factor and the measure of uncertainty of the empirical Bayes estimator to create a second-order Confidence Interval. Moreover, we present two simulation studies and a real data analysis to show the efficiency of this Confidence Interval.
Partha Lahiri - One of the best experts on this subject based on the ideXlab platform.
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a second order efficient empirical bayes Confidence Interval
Annals of Statistics, 2014Co-Authors: Masayo Yoshimori, Partha LahiriAbstract:We introduce a new adjusted residual maximum likelihood method (REML) in the context of producing an empirical Bayes (EB) Confidence Interval for a normal mean, a problem of great interest in different small area applications. Like other rival empirical Bayes Confidence Intervals such as the well-known parametric bootstrap empirical Bayes method, the proposed Interval is second-order correct, that is, the proposed Interval has a coverage error of order $O(m^{-{3}/{2}})$. Moreover, the proposed Interval is carefully constructed so that it always produces an Interval shorter than the corresponding direct Confidence Interval, a property not analytically proved for other competing methods that have the same coverage error of order $O(m^{-{3}/{2}})$. The proposed method is not simulation-based and requires only a fraction of computing time needed for the corresponding parametric bootstrap empirical Bayes Confidence Interval. A Monte Carlo simulation study demonstrates the superiority of the proposed method over other competing methods.
Guangyong Zou - One of the best experts on this subject based on the ideXlab platform.
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Confidence Interval estimation for the bland altman limits of agreement with multiple observations per individual
Statistical Methods in Medical Research, 2013Co-Authors: Guangyong ZouAbstract:The limits of agreement (LoA) method proposed by Bland and Altman has become a standard for assessing agreement between different methods measuring the same quantity. Virtually, all method comparison studies have reported only point estimates of LoA due largely to the lack of simple Confidence Interval procedures. In this article, we address Confidence Interval estimation for LoA when multiple measurements per individual are available. Separate procedures are proposed for situations when the underlying true value of the measured quantity is assumed changing and when it is perceived as stable. A fixed number of replicates per individual is not needed for the procedures to work. As shown by the worked examples, the construction of these Confidence Intervals requires only quantiles from the standard normal and chi-square distributions. Simulation results show the proposed procedures perform well. A SAS macro implementing the methods is available on the publisher's website.
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Confidence Interval construction for a difference between two dependent intraclass correlation coefficients
Statistics in Medicine, 2009Co-Authors: Chinthanie F Ramasundarahettige, Allan Donner, Guangyong ZouAbstract:Inferences for the difference between two dependent intraclass correlation coefficients (ICCs) may arise in studies in which a sample of subjects are each assessed several times with a new device and a standard. The ICC estimates for the two devices may then be compared using a test of significance. However, a Confidence Interval for a difference between two ICCs is more informative since it combines point estimation and hypothesis testing into a single inference statement. We propose a procedure that uses Confidence limits for a single ICC to recover variance estimates needed to set Confidence limits for the difference. An advantage of this approach is that it provides a Confidence Interval that reflects the underlying sampling distribution. Simulation results show that this method performs very well in terms of overall coverage percentage and tail errors. Two data sets are used to illustrate this procedure.
