Inference for linear exponential distribution based on extreme ranked set sampling

Hend S. Ali; Mostafa M. Mohie El-Din; S.M. Elarishy; Haidy A. Newer

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Inference for linear exponential distribution based on extreme ranked set sampling

Author(s)

Hend S. Ali Department of Mathematics, Faculty of Science, Al-Azhar University, Cairo, Egypt; Department of Mathematics, Center of Basic Science, Misr University for Science and Technology, Giza, Egypt.

Mostafa M. Mohie El-Din Department of Mathematics, Faculty of Science, Al-Azhar University,Cairo,Egypt

S.M. Elarishy Department of Mathematics, Faculty of Science, Al-Azhar University, Cairo, Egypt

Haidy A. Newer Department of Mathematics, Faculty of Education, Ain-Shams University, Cairo, Egypt.

Abstract

In this article, maximum likelihood estimation and Bayes estimators are derived for linear exponential distribution based on one- and m-cycle extreme ranked set sampling and simple random sample. These estimators are compared via their biases and mean squared error. This is done with respect to both symmetric and asymmetric loss functions. A reallife data set and simulation study are used to illustrate our procedures. This unequivocally shows that, in all circumstances taken into consideration for this study, ranked set sampling is more effective than both extreme ranked set sampling and simple random sample. Over a high number of cycles, a significant improvement is shown.

Publication Details

Page(s) 415-432

DOI DOI not available

Published Journal: Pakistan Journal of Statistics, Volume: 39, Issue: 3, Year: 2023

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Keywords

maximum likelihood estimation, Mean squared error, Bayesian estimation, Extreme ranked set sampling, Symmetric and asymmetric loss functions, Linear exponential distribution.

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