Tests for change-points for location or regression models are often based on cumulative sums of recursive residuals. In the context of general estimable parameters (functionals of the underlying distributions), such recursive residuals may be defined in terms of recursive U-statistics. Along with some invariance principles for recursive U-statistics, asymptotic properties of some proposed tests...

In a recent paper, Hughes (1999) showed that the power of tests of linear regression parameters could be improved by utilizing one-sided information regarding the nuisance parameters in the testing problem. In this paper, we extend this principle to the problemof diagnosing departures from the assumption of normality in linear regression residuals. We show that the asymptotic theory of the popu...

If a data set suffers from selection bias then, ordinary regression estimators must be adapted in order to achieve consistency. Consequently, it is necessary to modify any goodness of fit tests based on distances between parametric and non-parametric residuals. More particularly, any bootstrap approximation to these tests must be re– designed. Our first approach to this latter task is based on ...

We suggest an improved GMM estimator for the autoregressive parameter of a spatial autoregressive error model by taking into account that unobservable regression disturbances are different from observable regression residuals.

The primary models of interest for areal data analysis are regression models. In the same way that geo-regression models were used to study relations among continuous-data attributes of selected point locations (such as the California rainfall example), the present spatial regression models are designed to study relations among attributes of areal units (such as the English Mortality example in...