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 present a test for the composite hypothesis of normality against general alternatives. The test statistic is essentially the difference of two estimates of scale based on the empirical characteristic function. The test is simple to apply and its performance is comparable with that of several...

The problem of estimating the first positive zero of the real part of a characteristic function is discussed. Knowledge of the location of this zero is essential for the application of inferential procedures based on the empirical characteristic function. A simple explicit nonparametric...

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...

The distribution of the stochastic component of semi- and non-parametric models is often assumed to belong to a large class of distributions. In such models, the identifiability of the structural component of the model becomes important. For example, in the location problem, the class is...

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...