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A Comparative Study of the Gini Coefficient Estimators Based on the Linearization and U-Statistics Methods

Abstract: In this paper, we consider two well-known methods for analysis of the Gini index, which are U-statistics and linearization for some income distributions. In addition, we evaluate two different methods for some properties of their proposed estimators. Also, we

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Entropy Estimation From Ranked Set Samples With Application to Test of Fit
Ehsan Zamanzade M. Mahdizadeh

Abstract: This article deals with entropy estimation using ranked set sampling (RSS). Some estimators are developed based on the empirical distribution function and its nonparametric maximum likelihood competitor. The suggested entropy estimators have smaller root mean

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Monitoring Aggregated Poisson Data for Processes with Time-Varying Sample Sizes

Abstract: This article deals with the effect of data aggregation, when Poisson processes with varying sample sizes, are monitored. These aggregation procedures are necessary or convenient in many applications, and can simplify monitoring processes. In health surveillan

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A Review of Estimation of Key Parameters and Lead Time in Cancer Screening

Abstract: Early detection combined with effective treatment are the only ways to fight against cancer, and cancer screening is the primary technique for early detection. Although mass cancer screening has been carried out for decades, there are many unsolved problems,

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Goodness of Fit Tests for Rayleigh Distribution Based on Phi-Divergence
Ehsan Zamanzade M. Mahdizadeh

Abstract: In this paper, we develop some goodness of fit tests for Rayleigh distribution based on Phi-divergence. Using Monte Carlo simulation, we compare the power of the proposed tests with some traditional goodness of fiut tests including Kolmogorov-Smirnov, Anderso

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Local Dependence in Bivariate Copulae with Beta Marginals

Abstract: The local dependence function (LDF) describes changes in the correlation structure of continuous bivariate random variables along their range. Bivariate density functions with Beta marginals can be used to model jointly a wide variety of data with bounded out

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