An Improved Estimation of Parameter of Morgenstern-Type Bivariate Exponential Distribution Using Ranked Set Sampling

2022 ◽  
pp. 1-25
Author(s):  
Vishal Mehta
Keyword(s):  
Exponential Distribution ◽  
Mean Squared Error ◽  
Ranked Set Sampling ◽  
Error Estimator ◽  
Bivariate Exponential Distribution ◽  
Ranked Set Sample ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
Bivariate Exponential ◽  
Special Cases

In this chapter, the authors suggest some improved versions of estimators of Morgenstern type bivariate exponential distribution (MTBED) based on the observations made on the units of ranked set sampling (RSS) regarding the study variable Y, which is correlated with the auxiliary variable X, where (X,Y) follows a MTBED. In this chapter, they firstly suggested minimum mean squared error estimator for estimation of 𝜃2 based on censored ranked set sample and their special case; further, they have suggested minimum mean squared error estimator for best linear unbiased estimator of 𝜃2 based on censored ranked set sample and their special cases; they also suggested minimum mean squared error estimator for estimation of 𝜃2 based on unbalanced multistage ranked set sampling and their special cases. Efficiency comparisons are also made in this work.

scholarly journals Some Estimators of the Dispersion Parameter of a Chi-distributed Radial Error with Applications to Target Analysis

2016 ◽  
Vol 34 (1) ◽  
Author(s):  
Sharad Saxena ◽  
Housila P. Singh
Keyword(s):  
Mean Squared Error ◽  
Practical Importance ◽  
Dispersion Parameter ◽  
Point Estimate ◽  
Error Estimator ◽  
Shrinkage Estimators ◽  
Target Analysis ◽  
Radial Error ◽  
Minimum Mean Squared Error ◽  
Squared Error

The dispersion parameter of a chi-distributed radial error is of interest in numerous target analysis problems as a measure of weapon-system accuracy, and it is often of practical importance to estimate it. This paper presents a few classical estimators including the maximum likelihood estimator, an unbiased estimator and a minimum mean squared error estimator of this dispersion for both when the origin or “center of impact” is knownor can be assumed as known and when it is unknown. Some families of shrinkage estimators have also been suggested when a prior point estimate of the dispersion parameter is available in addition to sample information. The estimators of circular error probable and spherical error probable have been obtained as well. A simulation study has been carried out to demonstrate the performance of the proposed estimators.

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