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Showing 5 results for Zamanzade
Ehsan Zamanzadeh, Naser Arghami,
Volume 2, Issue 2 (2-2009)
Abstract
In this paper, we first introduce two new entropy estimators. These estimators are obtained by correcting Corea(1995)'s estimator in the extreme points and also assigning different weights to the end points.We then make a comparison among our proposed new entropy estimators and the entropy estimators proposed by Vasicek (1976), Ebrahimi, et al. (1994) and Corea(1995). We also introduce goodness of fit tests for exponentiality and normality based on our proposed entropy estimators. Results of a simulation study show that the proposed estimators and goodness of fit tests have good performances in comparison with the leading competitors.
Elham Zamanzadeh, Jafar Ahmadi,
Volume 5, Issue 1 (9-2011)
Abstract
In this paper, first a brief introduction of ranked set sampling is presented. Then, construction of confidence intervals for a quantile of the parent distribution based on ordered ranked set sample is given. Because the corresponding confidence coefficient is an step function, one may not be able to find the exact prescribed value. With this in mind, we introduce a new method and show that one can obtained an optimal confidence interval by appealing the proposed approach. We also compare the proposed scheme with the other existence methods.
Ehsan Zamanzade,
Volume 7, Issue 1 (9-2013)
Abstract
In this paper, two new entropy estimators are proposed. Then, entropy-based tests of exponentiality based on our entropy estimators are introduced. Simulation results show that the proposed estimators and related goodness of fit tests have good performances in comparison with their leading competitors.
Ehsan Zamanzade,
Volume 8, Issue 2 (3-2015)
Abstract
In this paper, an improved mean estimator for unbalanced ranked set samples is proposed. The estimator is obtained by using the fact that distribution function of order statistics are stochastically ordered. Also, it is showed that this estimator is convergent and has better performance than its empirical counterpart in unbalanced ranked set samples.
Hamed Mohamadghasemi, Ehsan Zamanzade, Mohammad Mohammadi,
Volume 10, Issue 1 (8-2016)
Abstract
Judgment post stratification is a sampling strategy which uses ranking information to give more efficient statistical inference than simple random sampling. In this paper, we introduce a new mean estimator for judgment post stratification. The estimator is obtained by using ordering observations in post strata. Our simulation results indicate that the new estimator performs better than its leading competitors in the literature.
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