On Inference of Overlapping Coefficients in Two<i>Lomax</i>Populations Using Different Sampling Methods

Helu, Amal; Samawi, Hani M.

doi:10.1080/15598608.2011.10483739

Cited by 9 publications

(6 citation statements)

References 31 publications

(30 reference statements)

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“…As in Helu and Samawi [16], parallel results to those of the two Lomax populations can be established for the inverse Lomax populations. Suppose (X ij ; j = 1, … , n i ; i = 1, 2) denote independent observations from two independent inverse Lomex populations.…”

Section: Estimationsupporting

confidence: 71%

See 1 more Smart Citation

On Inference of Overlapping Coefficients in Two Inverse Lomax Populations

Dhaker¹,

Dème²,

El-Adlouni³

2021

JSTA

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Overlapping coefficient is a direct measure of similarity between two distributions which is recently becoming very useful. This paper investigates estimation for some well-known measures of overlap, namely Matusita's measure , Weitzman's measure ∆ and Λ based on Kullback Leibler. Two estimation methods considered in this study are point estimation and Bayesian approach. Two inverse Lomax populations with different shape parameters are considered. The bias and mean square error properties of the estimators are studied through a simulation study and a real data example.

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Section: Estimationsupporting

confidence: 71%

“…The first method (RSS, McIntyre [15]) was earlier applied by Helu and Samawi [16] for the point and interval estimation of the overlapping coefficients for two Lomax distributions. We will use their methodology for the point estimate and interval in the case of inverse Lomax distribution.…”

Section: Introductionmentioning

confidence: 99%

On Inference of Overlapping Coefficients in Two Inverse Lomax Populations

Dhaker¹,

Dème²,

El-Adlouni³

2021

JSTA

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show abstract

“…As in Helu and Samawi [6], parallel results to those of the two Lomax populations can be established for the inverse Lomax populations. Suppose (X i j ; j = 1, ..., n i ; i = 1, 2) denote independent observations from two independent inverse Lomex populations.…”

Section: Estimationsupporting

confidence: 71%

“…In this article, we consider the point and interval estimation for some measures of overlap (OVL) for two Inverse Lomax populations with different shape Parameters using "Simple Random Sample (SRS) and Ranked Set Sampling (RSS) and Bayesian methodology". The first method (RSS , McIntyre [13]) was earlier applied by Helu and Samawi [6] for the point and interval estimation of the overlapping coefficients for two Lomax distributions. We will use their methodology for the point estimate and interval in the case of inverse Lomax distribution.…”

Section: Introductionmentioning

confidence: 99%

On Inference of Overlapping Coefficients in Two Inverse Lomax Populations

Dhaker¹,

Dème²,

El-Adlouni³

2019

Preprint

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Overlapping coefficient is a direct measure of similarity between two distributions which is recently becoming very useful. This paper investigates estimation for some well-known measures of overlap, namely Matusita's measure ρ, Weitzman's measure ∆ and Λ based on Kullback-Leibler. Two estimation methods considered in this study are point estimation and Bayesian approach. Two Inverse Lomax populations with different shape parameters are considered. The bias and mean square error properties of the estimators are studied through a simulation study and a real data example.

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“…23 , a major reason why OVL has not been widely used in practice is partially due to the lack of methods for confidence interval (CI) estimation. In most of the existing literature, parametric methods 12,[25][26][27]14,[28][29][30] have not yet been applied to general Gaussian (i.e. without equal mean or equal variance assumptions) or mixture Gaussian distributions, while non-parametric methods only focused on the point estimator for the cases with large sample sizes 7,31,14,32 .…”

Section: Introductionmentioning

confidence: 99%

Inference on the overlap coefficient: The binormal approach and alternatives

Franco-Pereira

Nakas

Reiser

et al. 2021

Stat Methods Med Res

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The overlap coefficient ([Formula: see text]) measures the similarity between two distributions through the overlapping area of their distribution functions. Given its intuitive description and ease of visual representation by the straightforward depiction of the amount of overlap between the two corresponding histograms based on samples of measurements from each one of the two distributions, the development of accurate methods for confidence interval construction can be useful for applied researchers. The overlap coefficient has received scant attention in the literature since it lacks readily available software for its implementation, while inferential procedures that can cover the whole range of distributional scenarios for the two underlying distributions are missing. Such methods, both parametric and non-parametric are developed in this article, while R-code is provided for their implementation. Parametric approaches based on the binormal model show better performance and are appropriate for use in a wide range of distributional scenarios. Methods are assessed through a large simulation study and are illustrated using a dataset from a study on human immunodeficiency virus-related cognitive function assessment.

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On Inference of Overlapping Coefficients in TwoLomaxPopulations Using Different Sampling Methods

Cited by 9 publications

References 31 publications

On Inference of Overlapping Coefficients in Two Inverse Lomax Populations

On Inference of Overlapping Coefficients in Two Inverse Lomax Populations

On Inference of Overlapping Coefficients in Two Inverse Lomax Populations

Inference on the overlap coefficient: The binormal approach and alternatives

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