Confidence interval estimation for lognormal data with application to health economics

Zou, Guang Yong; Taleban, Julia; Huo, Cindy Yan

doi:10.1016/j.csda.2009.03.016

Cited by 91 publications

(52 citation statements)

References 61 publications

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“…The MOVER and square-and-add approaches are evidently identical, yet neither is the original use of the method. Zou et al [12] pointed out that methods similar to MOVER have previously been introduced by Howe [13] for approximate CIs for the mean of the sum of two independent random variables, Graybill and Wang [14] for CIs on non-negative linear combinations of variances, and Lee et al [15] for CIs for linear combinations of variance components. These earlier works provided the rationale behind MOVER.…”

mentioning

confidence: 99%

Confidence intervals for a difference between proportions based on paired data

Tang

Ling

et al. 2009

Statistics in Medicine

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We construct several explicit asymptotic two-sided confidence intervals (CIs) for the difference between two correlated proportions using the method of variance of estimates recovery (MOVER). The basic idea is to recover variance estimates required for the proportion difference from the confidence limits for single proportions. The CI estimators for a single proportion, which are incorporated with the MOVER, include the Agresti-Coull, the Wilson, and the Jeffreys CIs. Our simulation results show that the MOVER-type CIs based on the continuity corrected Phi coefficient and the Tango score CI perform satisfactory in small sample designs and spare data structures. We illustrate the proposed CIs with several real examples.

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mentioning

confidence: 99%

Confidence intervals for a difference between proportions based on paired data

Tang

Ling

et al. 2009

Statistics in Medicine

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“…As the MOVER is relatively new, we shall first describe this approach as given in [17], [18], and [19]. Consider a set of parameters θ 1 , ...., θ g .…”

Section: Mover Confidence Intervalsmentioning

confidence: 99%

Improved Confidence Intervals for the Ratio of Coefficients of Variation of Two Lognormal Distributions

Hasan¹,

Krishnamoorthy²

2017

JSTA

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The problem of estimating the ratio of coefficients of variation of two independent lognormal populations is considered. We propose two closed-form approximate confidence intervals (CIs), one is based on the method of variance estimate recovery (MOVER), and another is based on the fiducial approach. The proposed CIs are compared with another CI available in the literature. Our new confidence intervals are very satisfactory in terms of coverage properties even for small samples, and better than other CIs for small to moderate samples. The methods are illustrated using an example.

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“…This is due to the calculation of the number of degrees of freedom 2  and 3  . We now propose an easy method, based on Zou et al [11][12], to construct a new confidence interval for  with one variance unknown. Our proposed confidence interval constructed using the test statistics Z (standard normal distribution) and T with degrees of freedom m-1.…”

Section: Ms CImentioning

confidence: 99%

“…Our proposed confidence interval constructed using the test statistics Z (standard normal distribution) and T with degrees of freedom m-1. Zou et al [11][12] proposed the method called "The Method of Variance Estimates Recovery (MOVER)" to construct confidence intervals for lognormal and normal data. Their strategy is to recover variance estimates from confidence interval of each parameter and then approximate confidence intervals for functions of parameters by using the central limit theorem.…”

Section: Ms CImentioning

confidence: 99%

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A Simple Confidence interval for the difference of two normal population means with one variance unknown

Niwitpong

2013

International Journal of Advanced Statistics and Probability

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In this paper, asymptotic coverage probabilities and expected lengths of confidence intervals for the difference between the means of two normal populations with one variance unknown are derived. Monte Carlo simulations results indicate that our proposed confidence interval, which is easy to use, performs as well as the existing confidence intervals.Keywords: confidence interval, coverage probability, expected length, normal means. IntroductionRecently, confidence intervals for the difference between two normal population means have been investigated. it is known that a confidence interval based on the t-distribution with pooled sample variances is preferable when it is assumed that two population variances are equal; otherwise, the Welch-Satterthwaite (WS, hereafter) confidence interval is preferable, see e.g. biased estimator of the number of degrees of freedom. Hence, they proposed an unbiased degrees of freedom of the t-test statistic which is described in section 2.2. Peng and Tong [9] showed that the t-test statistic with the unbiased estimator of the number of degrees of freedom 3  performs better that that of the Maity and Sherman's method especially when the variance of the unknown variance is large. In practice, both test statistics, however, need time to compute the number of degrees of freedom. In this paper, we therefore, propose a simple and easy method to construct the confidence interval with one variance unknown as in Niwitpong [10]. We also proved the coverage probability and the expected length of each confidence interval in comparison with the WS confidence interval. The paper is organized as follows. Section 2 presents confidence intervals for the difference between two normal population means with one variance unknown. Coverage probabilities and expected lengths of confidence intervals in section 2 are derived in section 3. Section 4 contains a discussion of the results and conclusions.

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Confidence interval estimation for lognormal data with application to health economics

Cited by 91 publications

References 61 publications

Confidence intervals for a difference between proportions based on paired data

Confidence intervals for a difference between proportions based on paired data

Improved Confidence Intervals for the Ratio of Coefficients of Variation of Two Lognormal Distributions

A Simple Confidence interval for the difference of two normal population means with one variance unknown

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