Abstract We consider linear models with scalar responses and covariates from a separable Hilbert space. The aim is to detect change points in the error distribution, based on sequential residual empirical distribution functions. Expansions for those estimated functions are more challenging in...

We consider linear models with scalar responses and covariates from a separable Hilbert space. The aim is to detect change points in the error distribution, based on sequential residual empirical distribution functions. Expansions for those estimated functions are more challenging in models with...

We consider the problem of predicting values of a random process or field satisfying a linear model y(x)=θ⊤f(x)+ε(x), where errors ε(x)are correlated. This is a common problem in kriging, where the case of discrete observations is standard. By focussing on the case of continuous...