ASYMPTOTIC DISTRIBUTIONS OF SEMIPARAMETRIC MAXIMUM LIKELIHOOD ESTIMATORS WITH ESTIMATING EQUATIONS FOR GROUP-CENSORED DATA

2005 ◽  
Vol 47 (2) ◽  
pp. 173-192 ◽  
Author(s):  
Di Chen ◽  
Jye-Chyi Lu ◽  
Shu Chuan Lin
Keyword(s):  
Maximum Likelihood ◽  
Censored Data ◽  
Maximum Likelihood Estimators ◽  
Estimating Equations ◽  
Asymptotic Distributions

Inference on P(Y

2020 ◽  
Vol 72 (2) ◽  
pp. 89-110
Author(s):  
Manoj Chacko ◽  
Shiny Mathew
Keyword(s):  
Maximum Likelihood ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Maximum Likelihood Estimators ◽  
Record Values ◽  
Asymptotic Distributions ◽  
Bayes Estimators ◽  
Generalized Pareto ◽  
Pareto Distributions ◽  
Generalized Pareto Distributions

In this article, the estimation of [Formula: see text] is considered when [Formula: see text] and [Formula: see text] are two independent generalized Pareto distributions. The maximum likelihood estimators and Bayes estimators of [Formula: see text] are obtained based on record values. The Asymptotic distributions are also obtained together with the corresponding confidence interval of [Formula: see text]. AMS 2000 subject classification: 90B25

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.

Statistical Inference of Stress–Strength Reliability of a Parallel System with Mixed Standby Components

2019 ◽  
Vol 26 (06) ◽  
pp. 1950030
Author(s):  
M. Kumar ◽  
K. C. Siju
Keyword(s):  
Maximum Likelihood ◽  
Confidence Interval ◽  
Gibbs Sampling ◽  
Maximum Likelihood Estimators ◽  
Sampling Procedure ◽  
Parallel System ◽  
Asymptotic Distributions ◽  
Bootstrap Estimate ◽  
Weibull Stress ◽  
Bayesian Estimators

This paper presents the Bayesian analysis of the stress–strength reliability of a parallel system with active and mixed standby components. The Bayesian estimators of the parameters of exponential and Weibull stress–strength distributions are obtained by assuming informative and noninformative prior for the parameters of the respective distributions. The Gibbs sampling procedure is used to compute the Bayesian stress–strength reliability. The asymptotic distributions, of the maximum likelihood estimators of the parameters of the corresponding distributions, and of the respective system reliabilities are presented. The bootstrap estimate and confidence interval are also obtained. The procedures are illustrated based on certain datasets.

scholarly journals Skewness of Maximum Likelihood Estimators in the Weibull Censored Data

Symmetry ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1351 ◽  
Author(s):  
Tiago M. Magalhães ◽  
Diego I. Gallardo ◽  
Héctor W. Gómez
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Sample Size ◽  
Censored Data ◽  
Maximum Likelihood Estimators ◽  
Monte Carlo Study ◽  
Parameter Distribution ◽  
Regression Parameter ◽  
Skewness Coefficient ◽  
Matrix Formula

In this paper, we obtain a matrix formula of order n − 1 / 2 , where n is the sample size, for the skewness coefficient of the distribution of the maximum likelihood estimators in the Weibull censored data. The present result is a nice approach to verify if the assumption of the normality of the regression parameter distribution is satisfied. Also, the expression derived is simple, as one only has to define a few matrices. We conduct an extensive Monte Carlo study to illustrate the behavior of the skewness coefficient and we apply it in two real datasets.

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.

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