scholarly journals Strong Consistency of Approximate Maximum Likelihood Estimators with Applications in Nonparametrics

1985 ◽  
Vol 13 (3) ◽  
pp. 932-946 ◽  
Author(s):  
Jane-Ling Wang
Keyword(s):  
Maximum Likelihood ◽  
Strong Consistency ◽  
Maximum Likelihood Estimators ◽  
Approximate Maximum Likelihood

Strong Consistency of Maximum Likelihood Estimators n Exponential Sequential Model

2014 ◽  
Vol 519-520 ◽  
pp. 878-882
Author(s):  
Chang Ming Yin ◽  
Bo Hong Chen ◽  
Shuang Hua Liu
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Strong Consistency ◽  
Maximum Likelihood Estimators ◽  
Regression Parameter ◽  
Parameter Vector ◽  
Likelihood Estimator ◽  
Sequential Model ◽  
Mild Conditions ◽  
Strongly Consistent

For the exponential sequential model, we show that maximum likelihood estimator of regression parameter vector is asymptotically existence and strongly consistent under mild conditions

scholarly journals Shrinkage Estimators for the Exponential Scale Parameter under Multiply Type II Censoring

2016 ◽  
Vol 34 (1) ◽  
Author(s):  
Umesh Singh ◽  
Anil Kumar
Keyword(s):  
Maximum Likelihood ◽  
Exponential Distribution ◽  
Scale Parameter ◽  
Maximum Likelihood Estimators ◽  
Shrinkage Estimators ◽  
Type Ii ◽  
Approximate Maximum Likelihood ◽  
Type Ii Censoring ◽  
Mean Squared Errors

We consider the problem of estimating the scale parameter of an exponential distribution under multiply type II censoring when a prior point guess of the parameter value is available. Shrinkage estimators are obtained from the approximate maximum likelihood estimators proposed in Singh et al. (2004) and in Balasubramanian and Balakrishnan (1992). These estimators are then compared by their simulated mean squared errors.

Strong Consistency of Maximum Likelihood Estimators in Extreme-Maximum-Value Distribution Model

2014 ◽  
Vol 525 ◽  
pp. 671-676
Author(s):  
Chang Ming Yin ◽  
Bo Hong Chen ◽  
Shuang Hua Liu
Keyword(s):  
Maximum Likelihood ◽  
Strong Consistency ◽  
Maximum Likelihood Estimators ◽  
Value Distribution ◽  
Maximum Likelihood Estimates ◽  
Distribution Model ◽  
Parameter Vector ◽  
Mild Conditions ◽  
Maximum Value ◽  
Strongly Consistent

For the extreme-maximum-value distribution model, we show that maximum likelihood estimates of regression parameter vector is asymptotically existence and strongly consistent under mild conditions

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.

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