Maximum likelihood estimators of population parameters from multiply censored samples

2009 ◽  
Vol 20 (3) ◽  
pp. 312-330 ◽  
Author(s):  
Abou El-Makarim A. Aboueissa
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimators ◽  
Population Parameters ◽  
Censored Samples

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
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimators ◽  
Population Parameters ◽  
Censored Samples

On the Strong Consistency of the Maximum-Likelihood Estimators from Randomly-Censored Samples

1997 ◽  
Vol 04 (01) ◽  
pp. 35-53
Author(s):  
Jin Wang ◽  
Jiading Chen
Keyword(s):  
Maximum Likelihood ◽  
Strong Consistency ◽  
Sufficient Conditions ◽  
Maximum Likelihood Estimators ◽  
Random Censoring ◽  
Censored Samples ◽  
Life Distributions ◽  
Strongly Consistent ◽  
Consistent Application ◽  
Randomly Censored

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.

scholarly journals The Combined-Unified Hybrid Censored Samples from Pareto Distribution: Estimation and Properties

2021 ◽  
Vol 11 (13) ◽  
pp. 6000
Author(s):  
Khalaf S. Sultan ◽  
Walid Emam
Keyword(s):  
Maximum Likelihood ◽  
Pareto Distribution ◽  
Maximum Likelihood Estimators ◽  
Real Data ◽  
Maximum Likelihood Estimates ◽  
Unknown Parameters ◽  
Hazard Functions ◽  
Hybrid Censoring ◽  
Censored Samples ◽  
Distribution Estimation

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.

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