2.2 Maximum Likelihood Estimators for Censored Samples of a progressively censored sample from a gamma

2016 ◽  
pp. 137-138
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimators ◽  
Censored Samples ◽  
Censored Sample

scholarly journals On the Existence and Uniqueness of the Maximum Likelihood Estimators of Normal and Lognormal Population Parameters with Grouped Data

2009 ◽  
Vol 2009 ◽  
pp. 1-16 ◽  
Author(s):  
Jin Xia ◽  
Jie Mi ◽  
YanYan Zhou
Keyword(s):  
Maximum Likelihood ◽  
Lognormal Distribution ◽  
Existence And Uniqueness ◽  
Maximum Likelihood Estimators ◽  
Invariance Property ◽  
Type I ◽  
Grouped Data ◽  
Sample Data ◽  
Censored Sample ◽  
Two Parameters

Lognormal distribution has abundant applications in various fields. In literature, most inferences on the two parameters of the lognormal distribution are based on Type-I censored sample data. However, exact measurements are not always attainable especially when the observation is below or above the detection limits, and only the numbers of measurements falling into predetermined intervals can be recorded instead. This is the so-called grouped data. In this paper, we will show the existence and uniqueness of the maximum likelihood estimators of the two parameters of the underlying lognormal distribution with Type-I censored data and grouped data. The proof was first established under the case of normal distribution and extended to the lognormal distribution through invariance property. The results are applied to estimate the median and mean of the lognormal population.

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 Weibull Inverse Lomax Distribution

2019 ◽  
pp. 587-603 ◽  
Author(s):  
Amal S Hassan ◽  
Rokaya E Mohamed
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimators ◽  
Real Data ◽  
Reliability Function ◽  
Model Parameters ◽  
Asymptotic Confidence Intervals ◽  
Censored Sample ◽  
Inverse Lomax Distribution ◽  
Sample Maximum ◽  
Type Ii Censored Sample

A four-parameter lifetime model, named the Weibull inverse Lomax (WIL) is presented and studied. Some structural properties are derived. The estimation of the model parameters is performed based on Type II censored sample. Maximum likelihood estimators along with asymptotic confidence intervals of population parameters and reliability function are constructed. The property of consistency of maximum likelihood estimators has been verified on the basis of simulated samples.  Further, the results are applied on two real data.

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
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