We address the important practical problem of selecting covariates in mixed linear models when the covariance structure is known from the data collection process and there are a possibly large number of covariates available. In particular, we consider procedures which can be considered...

We introduce bivariate quantiles which are defined through the bivariate distribution function. This approach ensures that, unlike most multivariate medians or the multivariate M-quartiles, the bivariate quantiles satisfy an analogous property to that of the univariate quantiles in that they...

We obtain a unform strong approximation for the distribution of a Nadaraya-Watson kernel estimator of a regression function. The approximation is obtained for general multivariate explanatory variables under an algebraic moment condition on the errors. A stronger rate of convergene result for...

The paper is concerned with estimating multivariate linear and autoregressive models using a generalisation of the functional least-squares procedure. This leads to a family of estimators, indexed by a vector parameter, for which strong uniform consistency and weak convergence results are...