AbstractA unified approach of treating multivariate linear normal models is presented. The results of the paper are based on a useful extension of the growth curve model. In particular, the finding of maximum likelihood estimators when linear restrictions exist on the parameters describing the mean in the growth curve model is considered. The problem with missing observations is also discussed and the EM algorithm is applied. Furthermore, a multivariate covariance model is generalized
AbstractA new approach of estimating parameters in multivariate models is introduced. A fitting func...
A maximum likelihood (ML) estimation procedure is developed to find the mean of the exponential fami...
In this article models based on pq-dimensional normally distributed ran-dom vectors x are studied wi...
AbstractA unified approach of treating multivariate linear normal models is presented. The results o...
AbstractThis paper provides an exposition of alternative approaches for obtaining maximum- likelihoo...
Estimation in the multivariate context when the number of observations available is less than the nu...
AbstractAn extended growth curve model is considered which, among other things, is useful when linea...
In this paper, we consider a multivariate linear model with complete/incomplete data, where the regr...
The problem of estimating the parameters of multivariate linear models in the context of an arbitrar...
AbstractWe propose a class of robust estimates for multivariate linear models. Based on the approach...
We propose a class of robust estimates for multivariate linear models. Based on the approach of MM-e...
Reduced-rank restrictions can add useful parsimony to coefficient matrices of multivariate models, b...
International audienceThe problem of estimating the parameters of multivariate linear models in the ...
AbstractThe growth curve model (Potthoff and Roy, 1964) and an extension (von Rosen, 1989) are consi...
A new approach of estimating parameters in multivariate models is introduced. A fitting function wil...
AbstractA new approach of estimating parameters in multivariate models is introduced. A fitting func...
A maximum likelihood (ML) estimation procedure is developed to find the mean of the exponential fami...
In this article models based on pq-dimensional normally distributed ran-dom vectors x are studied wi...
AbstractA unified approach of treating multivariate linear normal models is presented. The results o...
AbstractThis paper provides an exposition of alternative approaches for obtaining maximum- likelihoo...
Estimation in the multivariate context when the number of observations available is less than the nu...
AbstractAn extended growth curve model is considered which, among other things, is useful when linea...
In this paper, we consider a multivariate linear model with complete/incomplete data, where the regr...
The problem of estimating the parameters of multivariate linear models in the context of an arbitrar...
AbstractWe propose a class of robust estimates for multivariate linear models. Based on the approach...
We propose a class of robust estimates for multivariate linear models. Based on the approach of MM-e...
Reduced-rank restrictions can add useful parsimony to coefficient matrices of multivariate models, b...
International audienceThe problem of estimating the parameters of multivariate linear models in the ...
AbstractThe growth curve model (Potthoff and Roy, 1964) and an extension (von Rosen, 1989) are consi...
A new approach of estimating parameters in multivariate models is introduced. A fitting function wil...
AbstractA new approach of estimating parameters in multivariate models is introduced. A fitting func...
A maximum likelihood (ML) estimation procedure is developed to find the mean of the exponential fami...
In this article models based on pq-dimensional normally distributed ran-dom vectors x are studied wi...
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