Modified Maximum Likelihood Estimator of Scale Parameter Using Moving Extremes Ranked Set Sampling

2016 ◽  
Vol 45 (6) ◽  
pp. 2232-2240 ◽  
Author(s):  
Wangxue Chen ◽  
Minyu Xie ◽  
Ming Wu
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Scale Parameter ◽  
Ranked Set Sampling ◽  
Likelihood Estimator ◽  
Modified Maximum Likelihood ◽  
Modified Maximum Likelihood Estimator

scholarly journals A flexible ranked set sampling schemes: Statistical analysis on scale parameter

2020 ◽  
Vol 9 (1) ◽  
pp. 189-203
Author(s):  
Abbas Eftekharian ◽  
Mostafa Razmkhah ◽  
Jafar Ahmadi
Keyword(s):  
Maximum Likelihood ◽  
Existence And Uniqueness ◽  
Sampling Methods ◽  
Scale Parameter ◽  
Likelihood Estimation ◽  
Ranked Set Sampling ◽  
Likelihood Estimator ◽  
Normal Distributions ◽  
Sampling Schemes ◽  
The Cost

A flexible ranked set sampling scheme including some various existing sampling methods  is proposed. This scheme may be used to minimize the  error of ranking and the cost of sampling. Based on the data obtained from this scheme, the maximum likelihood estimation as well as the Fisher information are studied for the  scale family of distributions. The existence and uniqueness of  the  maximum likelihood estimator  of the scale parameter of the exponential  and  normal distributions are  investigated. Moreover, the optimal scheme is derived via simulation and numerical computations.

Using ranked set sampling with extreme ranks in estimating the population proportion

2019 ◽  
Vol 29 (1) ◽  
pp. 165-177 ◽  
Author(s):  
Ehsan Zamanzade ◽  
M Mahdizadeh
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Finite Sample ◽  
Likelihood Estimator ◽  
Population Proportion ◽  
Health And Nutrition ◽  
Finite Sample Size ◽  
Using Data

This article studies the properties of the maximum likelihood estimator of the population proportion in ranked set sampling with extreme ranks. The maximum likelihood estimator is described and its asymptotic distribution is derived. Finite sample size properties of the estimator are investigated using simulation studies. It turns out that the proposed estimator is substantially more efficient than its simple random sampling and ranked set sampling analogs, as the true population proportion tends to zero/unity. The method is illustrated using data from the National Health and Nutrition Examination Survey.

scholarly journals Classical and Bayesian Approach in Estimation of Scale Parameter of Nakagami Distribution

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Kaisar Ahmad ◽  
S. P. Ahmad ◽  
A. Ahmed
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Bayesian Approach ◽  
Simulation Study ◽  
Bayesian Method ◽  
Scale Parameter ◽  
Loss Functions ◽  
Likelihood Estimator ◽  
R Software ◽  
Nakagami Distribution

Nakagami distribution is considered. The classical maximum likelihood estimator has been obtained. Bayesian method of estimation is employed in order to estimate the scale parameter of Nakagami distribution by using Jeffreys’, Extension of Jeffreys’, and Quasi priors under three different loss functions. Also the simulation study is conducted in R software.

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