H
Hammou El Barmi
Researcher at City University of New York
Publications - 54
Citations - 474
Hammou El Barmi is an academic researcher from City University of New York. The author has contributed to research in topics: Estimator & Stochastic ordering. The author has an hindex of 12, co-authored 53 publications receiving 448 citations. Previous affiliations of Hammou El Barmi include Baruch College & Kansas State University.
Papers
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Peakedness and peakedness ordering
Hammou El Barmi,Hari Mukerjee +1 more
TL;DR: Improved estimators of the DFs are provided, it is shown that they are consistent, derive the weak convergence of the estimators, compare them with the empirical estimator, and provide formulas for statistical inferences.
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On detecting change in likelihood ratio ordering
Chengjie Xiong,Hammou El Barmi +1 more
TL;DR: In this paper, a binary search procedure was proposed to detect the changepoints in the sequence of the ratios of probabilities and obtain the maximum likelihood estimators of two multinomial probability vectors under the assumption that the probability ratio sequence has a changepoint.
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Tests for stochastic ordering under biased sampling
TL;DR: A nonparametric test for stochastic ordering from size-biased data, allowing the pattern of the size bias to differ between the two samples, is developed in terms of a maximally selected local empirical likelihood statistic.
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A note on estimating a non-increasing density in the presence of selection bias
Hammou El Barmi,Paul I. Nelson +1 more
TL;DR: In this article, a non-parametric maximum likelihood estimator (NPMLE) f n of a nonincreasing probability density function f with distribution function F on the basis of a sample from a weighted distribution G with density given by g(x)=w(x)f(x)/μ(f,w), where w(u)>0 for all u and μ(m,m,w)=∫w(mf,mw),w),∫m(m),m)d u is the normalizing constant.
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New and improved estimators of distribution functions under second-order stochastic dominance
Hammou El Barmi,Dobrin Marchev +1 more
TL;DR: In this paper, the problem of estimating F 1 when F 2 is known was considered and a new class of uniformly strongly consistent estimators for the two distribution functions, where n 1 and n 2 are the sample sizes.