Add to ORKGIn this paper, we use a maximal invariant likelihood (MIL) to construct two likelihood ratio (LR) tests. The first involves testing for the inclusion of a non-linear regressor and the second involves testing of a linear regressor against the alternative of a non-linear regressor. We report the results of a Monte Carlo experiment that compares the size and power properties of the traditional LR tests with those of our proposed MIL based LR tests. Our simulation results show that in both cases the MIL based tests have more accurate asymptotic critical values and better behaved (i.e., better centred) power curves than their classical counterparts. 2 1
• This paper develops statistical inference in linear models, dealing with the theory of maximum lik...
In the context of the linear regression model in which some regression coefficients are of interest ...
In this paper, we consider the problem of estimation of a regression model with both linear and nonl...
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...
This paper considers a linear panel data model with reduced rank regressors and interactive fixed ef...
Optimal invariant tests for model discrimination exist when the two models under hypotheses represen...
The aim of this paper is to compare through simulation the likelihood ratio (LR) test with the most ...
In the context of multivariate linear regression (MLR) models, it is well known that commonly employ...
The aim of this paper is to compare, in terms of power through simulation, the likelihood ratio (LR)...
This paper is concerned with the problem of testing a subset of the parameters which characterize th...
We propose likelihood and restricted likelihood ratio tests for goodness-of-fit of nonlinear regress...
This paper presents general formulae for the likelihood ratio (LR), Wald (W), Lagrange multiplier (L...
AbstractWe propose likelihood and restricted likelihood ratio tests for goodness-of-fit of nonlinear...
Likelihood ratio tests for fixed model terms are proposed for the analysis of linear mixed models wh...
• This paper develops statistical inference in linear models, dealing with the theory of maximum lik...
In the context of the linear regression model in which some regression coefficients are of interest ...
In this paper, we consider the problem of estimation of a regression model with both linear and nonl...
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...
This paper considers a linear panel data model with reduced rank regressors and interactive fixed ef...
Optimal invariant tests for model discrimination exist when the two models under hypotheses represen...
The aim of this paper is to compare through simulation the likelihood ratio (LR) test with the most ...
In the context of multivariate linear regression (MLR) models, it is well known that commonly employ...
The aim of this paper is to compare, in terms of power through simulation, the likelihood ratio (LR)...
This paper is concerned with the problem of testing a subset of the parameters which characterize th...
We propose likelihood and restricted likelihood ratio tests for goodness-of-fit of nonlinear regress...
This paper presents general formulae for the likelihood ratio (LR), Wald (W), Lagrange multiplier (L...
AbstractWe propose likelihood and restricted likelihood ratio tests for goodness-of-fit of nonlinear...
Likelihood ratio tests for fixed model terms are proposed for the analysis of linear mixed models wh...
• This paper develops statistical inference in linear models, dealing with the theory of maximum lik...
In the context of the linear regression model in which some regression coefficients are of interest ...
In this paper, we consider the problem of estimation of a regression model with both linear and nonl...