Additive models based on backfitting estimators are among the most important recent contributions to modern statistical modelling. However, the statistical properties of backfitting estimators have received relatively little attention. Recently, J.-D. Opsomer and D. Ruppert (1997,Ann....

Recent studies have shown that the asymptotic performance of nonparametric curve estimators in the presence of measurement error will often be very much inferior to that when the observations are error-free. For example, deconvolution of Gaussian measurement error worsens the usual algebraic...

The accuracy of the binned kernel density estimator is studied for general binning rules. We derive mean squared error results for the closeness of this estimator to both the true density and the unbinned kernel estimator. The binning rule and smoothness of the kernel function are shown to...

Penalised-spline-based additive models allow a simple mixed model representation where the variance components control departures from linear models. The smoothing parameter is the ratio of the random-coefficient and error variances and tests for linear regression reduce to tests for zero...

We study the finite sample performance of kernel density estimators through exact mean integrated squared error formulas when the data belong to an infinite order moving average process. It is demonstrated that dependence can have a significant influence, even in situations where the asymptotic...