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Comparison Between Dependent and Independent Ranked Set Sampling Designs for Parametric Estimation with Applications

Comparison Between Dependent and Independent Ranked Set Sampling Designs for Parametric... This paper is concerned with the estimation problem using maximum likelihood method of estimation for the unknown parameters of exponetiated gumbel distribution based on neoteric ranked set sampling (NRSS) as a new modification of the usual ranked set sampling (RSS) technique. Numerical study is conducted to compare NRSS as a dependent ranked set sampling technique, with RSS, and median ranked set sampling as independent sampling techniques, and then the performance of RSS and its modifications will be compared with simple random sampling based on their mean square errors and efficiencies. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Data Science Springer Journals

Comparison Between Dependent and Independent Ranked Set Sampling Designs for Parametric Estimation with Applications

Annals of Data Science , Volume 10 (1) – Feb 1, 2023

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References (25)

Publisher
Springer Journals
Copyright
Copyright © Springer-Verlag GmbH Germany, part of Springer Nature 2020
ISSN
2198-5804
eISSN
2198-5812
DOI
10.1007/s40745-020-00302-z
Publisher site
See Article on Publisher Site

Abstract

This paper is concerned with the estimation problem using maximum likelihood method of estimation for the unknown parameters of exponetiated gumbel distribution based on neoteric ranked set sampling (NRSS) as a new modification of the usual ranked set sampling (RSS) technique. Numerical study is conducted to compare NRSS as a dependent ranked set sampling technique, with RSS, and median ranked set sampling as independent sampling techniques, and then the performance of RSS and its modifications will be compared with simple random sampling based on their mean square errors and efficiencies.

Journal

Annals of Data ScienceSpringer Journals

Published: Feb 1, 2023

Keywords: Neoteric ranked set sampling; Median ranked set sampling; Ranked set sampling; Maximum likelihood estimation

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