Probability Limits, Asymptotic Distributions, and Properties of Maximum Likelihood Estimators

Econometrics ◽  
1974 ◽  
pp. 84-144
Author(s):  
Phoebus J. Dhrymes
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimators ◽  
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.

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.

Maximum likelihood estimation for branching processes with immigration

1981 ◽  
Vol 13 (3) ◽  
pp. 498-509 ◽  
Author(s):  
B. R. Bhat ◽  
S. R. Adke
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Continuous Time ◽  
Strong Consistency ◽  
Branching Process ◽  
Branching Processes ◽  
Maximum Likelihood Estimators ◽  
Likelihood Estimation ◽  
Asymptotic Distributions ◽  
Branching Process With Immigration

This paper establishes the strong consistency of the maximum likelihood estimators of the parameters of discrete- and continuous-time Markov branching processes with immigration. The asymptotic distributions of the maximum likelihood estimators of the parameters of a Galton–Watson branching process with immigration are also obtained.

Maximum likelihood estimation for branching processes with immigration

1981 ◽  
Vol 13 (03) ◽  
pp. 498-509 ◽  
Author(s):  
B. R. Bhat ◽  
S. R. Adke
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Continuous Time ◽  
Strong Consistency ◽  
Branching Process ◽  
Branching Processes ◽  
Maximum Likelihood Estimators ◽  
Likelihood Estimation ◽  
Asymptotic Distributions ◽  
Branching Process With Immigration

This paper establishes the strong consistency of the maximum likelihood estimators of the parameters of discrete- and continuous-time Markov branching processes with immigration. The asymptotic distributions of the maximum likelihood estimators of the parameters of a Galton–Watson branching process with immigration are also obtained.

Asymptotic Inference on the Moving Average Impact Matrix in Cointegrated 1(1) VAR Systems

1997 ◽  
Vol 13 (1) ◽  
pp. 79-118 ◽  
Author(s):  
Paolo Paruolo
Keyword(s):  
Maximum Likelihood ◽  
Likelihood Ratio ◽  
Moving Average ◽  
Maximum Likelihood Estimators ◽  
Asymptotic Distributions ◽  
Likelihood Ratio Tests ◽  
Common Trend ◽  
Average Impact ◽  
The Common ◽  
Definition Of

This paper addresses the problem of inference on the moving average impact matrix and on its row and column spaces in cointegrated 1(1) VAR processes. The choice of bases (i.e., the identification) of these spaces, which is of interest in the definition of the common trend structure of the system, is discussed. Maximum likelihood estimators and their asymptotic distributions are derived, making use of a relation between properly normalized bases of orthogonal spaces, a result that may be of separate interest. Finally, Wald-type tests are given, and their use in connection with existing likelihood ratio tests is discussed.

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