Add to ORKGWe consider likelihood and Bayesian inferences for seemingly unrelated (linear) regressions for the joint multivariate t-error (e.g. Zellner, 1976) and the independent t-error (e.g. Maronna, 1976) models. For likelihood inferenc
In this dissertation, the conditions which determine the relative efficiency of ordinary least squar...
In this study we investigate the problem of estimation and testing of hypotheses in multivariate lin...
Likelihood based methods for obtaining approximate confidence intervals for the slope in a simple li...
ISBN 0734024487,Working paper 793.Zellner's idea of combining several equations into one model to im...
We consider likelihood-based inference from multivariate regression models with independent Student-...
Seemingly unrelated regression (SUR) models traditionally appear in econometrics but recently also ...
Chapter prepared for Computer-Aided Econometrics, edited by David Giles, to be published by Marcel D...
In this note estimation and prediction is considered for a (linear or nonlinear) regression model wi...
A method is presented for simultaneously estimating a system of nonparametric regressions which may...
Linear regression models are foundation of current statistical theory and have been a prominent obje...
Multicollinearity in linear regression is typically thought of as a problem of large standard errors...
In this paper, different approaches to dealing with nuisance parameters in likelihood based inferenc...
. A general linear model can be written as Y = XB 0 + U , where Y is an N \Theta p matrix of obser...
© 2018 Elsevier Inc. Seemingly unrelated regression models generalize linear regression models by co...
In latent variable models parameter estimation can be implemented by using the joint or the marginal...
In this dissertation, the conditions which determine the relative efficiency of ordinary least squar...
In this study we investigate the problem of estimation and testing of hypotheses in multivariate lin...
Likelihood based methods for obtaining approximate confidence intervals for the slope in a simple li...
ISBN 0734024487,Working paper 793.Zellner's idea of combining several equations into one model to im...
We consider likelihood-based inference from multivariate regression models with independent Student-...
Seemingly unrelated regression (SUR) models traditionally appear in econometrics but recently also ...
Chapter prepared for Computer-Aided Econometrics, edited by David Giles, to be published by Marcel D...
In this note estimation and prediction is considered for a (linear or nonlinear) regression model wi...
A method is presented for simultaneously estimating a system of nonparametric regressions which may...
Linear regression models are foundation of current statistical theory and have been a prominent obje...
Multicollinearity in linear regression is typically thought of as a problem of large standard errors...
In this paper, different approaches to dealing with nuisance parameters in likelihood based inferenc...
. A general linear model can be written as Y = XB 0 + U , where Y is an N \Theta p matrix of obser...
© 2018 Elsevier Inc. Seemingly unrelated regression models generalize linear regression models by co...
In latent variable models parameter estimation can be implemented by using the joint or the marginal...
In this dissertation, the conditions which determine the relative efficiency of ordinary least squar...
In this study we investigate the problem of estimation and testing of hypotheses in multivariate lin...
Likelihood based methods for obtaining approximate confidence intervals for the slope in a simple li...