Add to ORKGThis paper considers a nonlinear regression model, in which the dependent variable has the gamma distribution. A model is considered in which the shape parameter of the random variable is the sum of continuous and algebraically independent functions. The paper proves that there is exactly one maximum likelihood estimator for the gamma regression model
Maximum likelihood parameter estimation becomes easy by augmenting the parameter space of the probab...
Abstract: A profile likelihood inference is made for the regression coefficient and frailty paramete...
[[abstract]]A profile likelihood inference is made for the regression coefficient and frailty parame...
Abstract. This paper considers a nonlinear regression model, in which the dependent variable has the...
The subject of this article is to present the beta – regression model, where we assume that one para...
Abstract. The subject of this article is to present the beta – regression model, where we assume tha...
The statistical properties of the maximum likelihood estimator (M.L.E.) of the parameters of a linea...
In this paper, we consider the problem of estimation of a regression model with both linear and nonl...
Maximum Likelihood Estimation for Gamma Distribution and its Numerical Solution by Newton-Raphson an...
In this paper, we consider the multicollinearity problem in the gamma regression model when model pa...
The paper considers statistical inference for R = P(X \u3c Y) in the case when both X and Y have gen...
It is well-known that maximum likelihood (ML) estimators of the two parame- ters in a Gamma distribu...
The problem of estimation of an unknown shape parameter under the sample drawn from the gamma distr...
It has been shown that the uniformly minimum variance unbiased (UMVU) esti-mator of the generalized ...
The known linear regression model (LRM) is used mostly for modelling the QSAR relationship between t...
Maximum likelihood parameter estimation becomes easy by augmenting the parameter space of the probab...
Abstract: A profile likelihood inference is made for the regression coefficient and frailty paramete...
[[abstract]]A profile likelihood inference is made for the regression coefficient and frailty parame...
Abstract. This paper considers a nonlinear regression model, in which the dependent variable has the...
The subject of this article is to present the beta – regression model, where we assume that one para...
Abstract. The subject of this article is to present the beta – regression model, where we assume tha...
The statistical properties of the maximum likelihood estimator (M.L.E.) of the parameters of a linea...
In this paper, we consider the problem of estimation of a regression model with both linear and nonl...
Maximum Likelihood Estimation for Gamma Distribution and its Numerical Solution by Newton-Raphson an...
In this paper, we consider the multicollinearity problem in the gamma regression model when model pa...
The paper considers statistical inference for R = P(X \u3c Y) in the case when both X and Y have gen...
It is well-known that maximum likelihood (ML) estimators of the two parame- ters in a Gamma distribu...
The problem of estimation of an unknown shape parameter under the sample drawn from the gamma distr...
It has been shown that the uniformly minimum variance unbiased (UMVU) esti-mator of the generalized ...
The known linear regression model (LRM) is used mostly for modelling the QSAR relationship between t...
Maximum likelihood parameter estimation becomes easy by augmenting the parameter space of the probab...
Abstract: A profile likelihood inference is made for the regression coefficient and frailty paramete...
[[abstract]]A profile likelihood inference is made for the regression coefficient and frailty parame...