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Hamza Dhaker

Publications and source records attributed to Hamza Dhaker.

5 recordsLinked to original sources
arXiv2019

On Inference of Overlapping Coefficients in Two Inverse Lomax Populations

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 $Δ$ 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.

stat.ME↗
arXiv2019

Some improvement on non-parametric estimation of income distribution and poverty index

In this paper, we propose an estimator of Foster, Greer and Thorbecke class of measures $\displaystyle P(z,α) = \int_0^{z}\Big(\frac{z-x}{z}\Big)^αf(x)\, dx$, where $z>0$ is the poverty line, $f$ is the probabily density function of the income distribution and $α$ is the so-called poverty aversion. The estimator is constructed with a bias reduced kernel estimator. Uniform almost sure consistency and uniform mean square consistenty are established. A simulation study indicates that our new estimator performs well.

math.ST↗
arXiv2017

Overlap Coefficients Based on Kullback-Leibler Divergence: Exponential Populations Case

This article is devoted to the study of overlap measures of densities of two exponential populations. Various Overlapping Coefficients, namely: Matusita's measure $ρ$, Morisita's measure $λ$ and Weitzman's measure $Δ$. A new overlap measure $Λ$ based on Kullback-Leibler measure is proposed. The invariance property and a method of statistical inference of these coefficients also are presented. Taylor series approximation are used to construct confidence intervals for the overlap measures. The bias and mean square error properties of the estimators are studied through a simulation study.

stat.ME↗
arXiv2014

Comparaison between the two models : new approach using the $α$-divergence

We propose new nonparametric accordance Rényi-$α$ and $α$-Tsallis divergence estimators for continuous distributions. We discuss this approach with a view to the selection model (on alétoire and autoregressive AR (1)). We lestimateur used by kernel density esttimer underlying. Nevertheless, we are able to prove that the estimators are consistent under certain conditions. We also describe how to apply these estimators and demonstrate their effectiveness through numerical experiments.

stat.ME↗