Estimation of a regression function is a well-known problem in the context of errors in variables, where the explanatory variable is observed with random noise. This noise can be of two types, which are known as classical or Berkson, and it is common to assume that the error is purely of one of...

It is common, in errors-in-variables problems in regression, to assume that the errors are incurred 'after the experiment', in that the observed value of the explanatory variable is an independent perturbation of its true value. However, if the errors are incurred 'before the experiment' then...

type="main" xml:id="rssb12067-abs-0001" <title type="main">Summary</title> <p>Errors-in-variables regression is important in many areas of science and social science, e.g. in economics where it is often a feature of hedonic models, in environmental science where air quality indices are measured with error, in biology where...</p>

We suggest two new methods, which are applicable to both deconvolution and regression with errors in explanatory variables, for nonparametric inference. The two approaches involve kernel or orthogonal series methods. They are based on defining a low order approximation to the problem at hand,...

Increasingly, scientific studies yield functional data, in which the ideal units of observation are curves and the observed data consist of sets of curves that are sampled on a fine grid. We present new methodology that generalizes the linear mixed model to the functional mixed model framework,...

type="main" xml:id="rssb12066-abs-0001" <title type="main">Summary</title> <p>We consider heteroscedastic regression models where the mean function is a partially linear single-index model and the variance function depends on a generalized partially linear single-index model. We do not insist that the variance function...</p>