Maximum likelihood estimators of population parameters from doubly left-censored samples

2006 ◽  
Vol 17 (8) ◽  
pp. 811-826 ◽  
Author(s):  
Abou El-Makarim A. Aboueissa ◽  
Michael R. Stoline
Author(s):  
Jin Wang ◽  
Jiading Chen

In the randomly-censored model, we define Y = min (X, T) and Z = I{X < T}, where X is the life length, and T is the random censoring time which is independent of X. Couple (Y, Z) is observed. Sufficient conditions are found to ensure that the Maximum-Likelihood Estimators (MLE) are strongly consistent. Application is made to usual life distributions.


2021 ◽  
Vol 11 (13) ◽  
pp. 6000
Author(s):  
Khalaf S. Sultan ◽  
Walid Emam

In this paper, we use the combined-unified hybrid censoring samples to obtain the maximum likelihood estimates of the unknown parameters, survival, and hazard functions of Pareto distribution. Next, we discuss some efficiency criteria of the maximum likelihood estimators, including; the unbiasedness, consistency, and sufficiency. Additionally, we use MCMC to obtain the Bayesian estimates of the unknown parameters. In addition, we calculate the intervals estimation of the unknown parameters. Finally, we analyze a set of real data in view of the theoretical findings of the paper.


Sign in / Sign up

Export Citation Format

Share Document