New Parametric Estimation Methods based on Ranked Set Sampling

Year 2019, , 1356 - 1368, 01.12.2019

Mohamed Abdallah , Samir Ashour

https://doi.org/10.35378/gujs.477631

Cited By: 1

Abstract

The problem of parameters estimation plays a
significant role in various areas of academic researches. In this article, we
propose three different methods of estimation for the parameters of
location-scale family under ranked set sampling in the view of missing data
mechanism. Through a series of Monte Carlo simulations, it is well investigated
that the proposed methods are relatively robust from violating the perfect
ranking condition and provide better performance over their competitors using
bias and MSE (mean square error) criteria. An empirical data set is also used
for illustrative purposes.

Keywords

Cramér-von-Mises, EM algorithm, Estimation methods, Missing Data Approach, Ranked set sampling

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