This paper considers the cointegrating regression with errors whose variances change smoothly over time. The model can be used to describe a longrun cointegrating relationship, the tightness of which varies along with time. Heteroskedasticity in the errors is modelled nonparametrically and is...

We consider deconvolution problems where the observations are equal in distribution to X = [lambda]1E1 + ... + [lambda]mEm + Y, or to X = [mu]1L1 + ... + [mu]mLm + Y. Here the random variables in the sums are independent, the Ei are exponentially distributed, the Li are Laplace distributed and Y...

This paper is concerned with estimating the mean of a random variable Y conditional on a vector of covariates X under weak assumptions about the form of the conditional mean function. Fully nonparametric estimation is usually unattractive when X is multidimensional because estimation precision...

We consider possibly dependent data N1', ..., Nn' of the p-thinning N' of a point process N and construct estimators and of the thinning function p and of the Laplace functional of N. Under mixing conditions we prove the mean square convergence of , the uniform almost sure convergence of and and...