The statistical properties of the maximum likelihood estimator (M.L.E.) of the parameters of a linear model with Gamma distributed residuals are analysed. Some previous results of the literature on this topic are corrected, allowing the computation of the asymptotic relative efficiency of the M.L.E. over the O.L.S
The analysis of residuals can capture departures from a parametrized model. In this thesis we look a...
We consider a linear model with normally distributed but heteroscedastic errors. When the error vari...
The three-parameter log-gamma distribution is a versatile lifetime model. However, it has a quite un...
The statistical properties of the maximum likelihood estimator (M.L.E.) of the parameters of a linea...
The statistical properties of the maximum likelihood estimator (M.L.E.) of the parameters of a linea...
The statistical properties of the maximum likelihood estimator (M.L.E.) of the parameters of a linea...
The statistical properties of the maximum likelihood estimator (M.L.E.) of the parameters of a linea...
This paper considers a nonlinear regression model, in which the dependent variable has the gamma dis...
In completely specified models, where explicit formulae are derivable for the probabilities of obser...
In completely specified models, where explicit formulae are derivable for the probabilities of obser...
It is well-known that maximum likelihood (ML) estimators of the two parame- ters in a Gamma distribu...
Abstract. This paper considers a nonlinear regression model, in which the dependent variable has the...
Abstract. This paper considers a nonlinear regression model, in which the dependent variable has the...
We illustrate with examples when and how maximum likelihood estimators continue to be asymptotically...
Asymptotic expansions are made for the distributions of the Maximum Empirical Likelihood (MEL) estim...
The analysis of residuals can capture departures from a parametrized model. In this thesis we look a...
We consider a linear model with normally distributed but heteroscedastic errors. When the error vari...
The three-parameter log-gamma distribution is a versatile lifetime model. However, it has a quite un...
The statistical properties of the maximum likelihood estimator (M.L.E.) of the parameters of a linea...
The statistical properties of the maximum likelihood estimator (M.L.E.) of the parameters of a linea...
The statistical properties of the maximum likelihood estimator (M.L.E.) of the parameters of a linea...
The statistical properties of the maximum likelihood estimator (M.L.E.) of the parameters of a linea...
This paper considers a nonlinear regression model, in which the dependent variable has the gamma dis...
In completely specified models, where explicit formulae are derivable for the probabilities of obser...
In completely specified models, where explicit formulae are derivable for the probabilities of obser...
It is well-known that maximum likelihood (ML) estimators of the two parame- ters in a Gamma distribu...
Abstract. This paper considers a nonlinear regression model, in which the dependent variable has the...
Abstract. This paper considers a nonlinear regression model, in which the dependent variable has the...
We illustrate with examples when and how maximum likelihood estimators continue to be asymptotically...
Asymptotic expansions are made for the distributions of the Maximum Empirical Likelihood (MEL) estim...
The analysis of residuals can capture departures from a parametrized model. In this thesis we look a...
We consider a linear model with normally distributed but heteroscedastic errors. When the error vari...
The three-parameter log-gamma distribution is a versatile lifetime model. However, it has a quite un...
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