Model Robust Confidence Intervals Using Maximum Likelihood Estimators

1986 ◽  
Vol 54 (2) ◽  
pp. 221 ◽  
Author(s):  
Richard M. Royall
Keyword(s):  
Maximum Likelihood ◽  
Confidence Intervals ◽  
Maximum Likelihood Estimators

Estimation of the mean and the initial probabilities of a branching process

1974 ◽  
Vol 11 (4) ◽  
pp. 687-694 ◽  
Author(s):  
Jean-Pierre Dion
Keyword(s):  
Maximum Likelihood ◽  
Confidence Intervals ◽  
Branching Process ◽  
Maximum Likelihood Estimators ◽  
Asymptotic Distributions ◽  
The Mean ◽  
Watson Process

In this article, we consider maximum likelihood estimators of the initial probabilities and the mean of a supercritical Galton-Watson process; we find for these estimators, as well as for the Lotka-Nagaev estimator of the mean, the asymptotic distributions and deduce confidence intervals. As these results hold even if the independence between individuals of the same generation is not satisfied, application to living populations may be considered.

Estimation of the mean and the initial probabilities of a branching process

1974 ◽  
Vol 11 (04) ◽  
pp. 687-694 ◽  
Author(s):  
Jean-Pierre Dion
Keyword(s):  
Maximum Likelihood ◽  
Confidence Intervals ◽  
Branching Process ◽  
Maximum Likelihood Estimators ◽  
Asymptotic Distributions ◽  
The Mean ◽  
Watson Process

In this article, we consider maximum likelihood estimators of the initial probabilities and the mean of a supercritical Galton-Watson process; we find for these estimators, as well as for the Lotka-Nagaev estimator of the mean, the asymptotic distributions and deduce confidence intervals. As these results hold even if the independence between individuals of the same generation is not satisfied, application to living populations may be considered.

scholarly journals A Simple Random Sampling Modified Dual to Product Estimator for Estimating Population Mean using Order Statistics

2021 ◽  
Vol 19 (1) ◽  
pp. 2-32
Author(s):  
Sanjay Kumar ◽  
Priyanka Chhaparwal
Keyword(s):  
Maximum Likelihood ◽  
Order Statistics ◽  
Confidence Intervals ◽  
Random Sampling ◽  
Maximum Likelihood Estimators ◽  
Real Data ◽  
Simple Random Sampling ◽  
Data Set ◽  
Modified Maximum Likelihood ◽  
Product Estimator

Bandopadhyaya (1980) developed a dual to product estimator using robust modified maximum likelihood estimators (MMLE’s). Their properties were obtained theoretically and supported through simulations studies with generated as well as one real data set. Robustness properties in the presence of outliers and confidence intervals were studied.

Using Approximation Non-Bayesian Computation with Fuzzy Data to Estimation Inverse Weibull Parameters and Reliability Function

2018 ◽  
pp. 397
Author(s):  
Nadia Hashim Al-Noor ◽  
Shurooq A.K. Al-Sultany
Keyword(s):  
Maximum Likelihood ◽  
Expectation Maximization ◽  
Mean Squared Error ◽  
Maximum Likelihood Estimators ◽  
Reliability Function ◽  
Fuzzy Data ◽  
Monte Carlo Simulation Study ◽  
Weibull Parameters ◽  
Squared Error ◽  
Newton Raphson

        In real situations all observations and measurements are not exact numbers but more or less non-exact, also called fuzzy. So, in this paper, we use approximate non-Bayesian computational methods to estimate inverse Weibull parameters and reliability function with fuzzy data. The maximum likelihood and moment estimations are obtained as non-Bayesian estimation. The maximum likelihood estimators have been derived numerically based on two iterative techniques namely “Newton-Raphson” and the “Expectation-Maximization” techniques. In addition, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the parameters and reliability function in terms of their mean squared error values and integrated mean squared error values respectively.

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