In the context of a general regression model in which some regression coefficients are of interest and others are purely nuisance parameters, we define the density function of a maximal invariant statistic with the aim of testing for the inclusion of regressors (either linear or non-linear) in linear or semi-linear models. This allows the construction of the locally best invariant test, which in two important cases is equivalent to the one-sided t test for a regression coefficient in an artificial linear regression model.We consider a specific semi-linear model to apply the constructed test
In this paper, we consider the problem of estimation of a regression model with both linear and nonl...
This paper considers a linear panel data model with reduced rank regressors and interactive fixed ef...
Inference on the autocorrelation coefficient p of a linear regression model with first-order autoreg...
In the context of the linear regression model in which some regression coefficients are of interest ...
In the context of the linear regression model in which some regression coefficients are of interest ...
In the context of the linear regression model in which some regression coefficients are of interest ...
In the context of the linear regression model in which some regression coefficients are of interest ...
In this paper, we use a maximal invariant likelihood (MIL) to construct two likelihood ratio (LR) te...
In this paper, we use a maximal invariant likelihood (MIL) to construct two likelihood ratio (LR) te...
In this paper, we use a maximal invariant likelihood (MIL) to construct two likelihood ratio (LR) te...
This paper considers a class of hypothesis testing problems concerning the covariance matrix of the ...
Optimal invariant tests for model discrimination exist when the two models under hypotheses represen...
In the context of a general regression model in which some regression coefficients are of interest a...
AbstractThe null hypothesis that the error vectors in a multivariate linear model are independent is...
This paper is concerned with the problem of testing a subset of the parameters which characterize th...
In this paper, we consider the problem of estimation of a regression model with both linear and nonl...
This paper considers a linear panel data model with reduced rank regressors and interactive fixed ef...
Inference on the autocorrelation coefficient p of a linear regression model with first-order autoreg...
In the context of the linear regression model in which some regression coefficients are of interest ...
In the context of the linear regression model in which some regression coefficients are of interest ...
In the context of the linear regression model in which some regression coefficients are of interest ...
In the context of the linear regression model in which some regression coefficients are of interest ...
In this paper, we use a maximal invariant likelihood (MIL) to construct two likelihood ratio (LR) te...
In this paper, we use a maximal invariant likelihood (MIL) to construct two likelihood ratio (LR) te...
In this paper, we use a maximal invariant likelihood (MIL) to construct two likelihood ratio (LR) te...
This paper considers a class of hypothesis testing problems concerning the covariance matrix of the ...
Optimal invariant tests for model discrimination exist when the two models under hypotheses represen...
In the context of a general regression model in which some regression coefficients are of interest a...
AbstractThe null hypothesis that the error vectors in a multivariate linear model are independent is...
This paper is concerned with the problem of testing a subset of the parameters which characterize th...
In this paper, we consider the problem of estimation of a regression model with both linear and nonl...
This paper considers a linear panel data model with reduced rank regressors and interactive fixed ef...
Inference on the autocorrelation coefficient p of a linear regression model with first-order autoreg...