Add to ORKGIn the context of the linear regression model in which some regression coefficients are of interest and others are purely nuisance parameters, we derive the density function of a maximal invariant statistic. This allows the construction of a range of optimal test statistics including the locally best invariant test which is equivalent to the well-known one-sided t-test. The approach is also extended to the general linear regression model in which the covariance matrix is nonscalar and to non-linear regression models
In this paper, we use a maximal invariant likelihood (MIL) to construct two likelihood ratio (LR) te...
In this paper, we consider the problem of estimation of a regression model with both linear and nonl...
We consider the general family of multivariate normal distributions where the mean vector lies in an...
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 a general regression model in which some regression coefficients are of interest a...
In the context of the linear regression model in which some regression coefficients are of interest ...
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...
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...
This paper considers a linear panel data model with reduced rank regressors and interactive fixed ef...
In this paper, we use a maximal invariant likelihood (MIL) to construct two likelihood ratio (LR) te...
In the context of a general regression model in which some regression coefficients are of interest a...
Inference on the autocorrelation coefficient p of a linear regression model with first-order autoreg...
In this paper, we use a maximal invariant likelihood (MIL) to construct two likelihood ratio (LR) te...
In this paper, we consider the problem of estimation of a regression model with both linear and nonl...
We consider the general family of multivariate normal distributions where the mean vector lies in an...
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 a general regression model in which some regression coefficients are of interest a...
In the context of the linear regression model in which some regression coefficients are of interest ...
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...
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...
This paper considers a linear panel data model with reduced rank regressors and interactive fixed ef...
In this paper, we use a maximal invariant likelihood (MIL) to construct two likelihood ratio (LR) te...
In the context of a general regression model in which some regression coefficients are of interest a...
Inference on the autocorrelation coefficient p of a linear regression model with first-order autoreg...
In this paper, we use a maximal invariant likelihood (MIL) to construct two likelihood ratio (LR) te...
In this paper, we consider the problem of estimation of a regression model with both linear and nonl...
We consider the general family of multivariate normal distributions where the mean vector lies in an...