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>

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="rssb12040-abs-0001" <title type="main">Summary</title> <p>Differential equations are customarily used to describe dynamic systems. Existing methods for estimating unknown parameters in those systems include parameter cascade, which is a spline-based technique, and pseudo-least-squares, which is a...</p>

type="main" xml:id="rssb12051-abs-0001" <title type="main">Summary</title> <p>We introduce a new method for improving the coverage accuracy of confidence intervals for means of lattice distributions. The technique can be applied very generally to enhance existing approaches, although we consider it in greatest detail in the...</p>

Many contemporary classifiers are constructed to provide good performance for very high dimensional data. However, an issue that is at least as important as good classification is determining which of the many potential variables provide key information for good decisions. Responding to this...

We develop a general non-parametric approach to the analysis of clustered data via random effects. Assuming only that the link function is known, the regression functions and the distributions of both cluster means and observation errors are treated non-parametrically. Our argument proceeds by...