Open AccessParameter Estimation Based on Double Ranked Set Samples with Applications to Weibull Distribution
Author(s) -
M. Sabry,
Hiba Z. Muhammed,
Mostafa Shaaban,
Abd El Hady Nabih
Publication year - 2022
Publication title -
journal of modern applied statistical methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.169
H-Index - 28
ISSN - 1538-9472
DOI - 10.22237/jmasm/1619482260
Subject(s) - mathematics , weibull distribution , statistics , estimator , rss , simple random sample , monte carlo method , likelihood function , sampling (signal processing) , estimation theory , computer science , population , demography , sociology , operating system , filter (signal processing) , computer vision
In this paper, the likelihood function for parameter estimation based on double ranked set sampling (DRSS) schemes is introduced. The proposed likelihood function is used for the estimation of the Weibull distribution parameters. The maximum likelihood estimators (MLEs) are investigated and compared to the corresponding ones based on simple random sampling (SRS) and ranked set sampling (RSS) schemes. A Monte Carlo simulation is conducted and the absolute relative biases, mean square errors, and efficiencies are compared for the different schemes. It is found that, the MLEs based on DRSS is more efficient than MLE using SRS and RSS for estimating the two parameters of the Weibull distribution (WD).