We propose and study a kernel estimator of a density in which the kernel is adapted to the data but not fixed. The smoothing procedure is followed by a location-scale transformation to reduce bias and variance. The new method naturally leads to an adaptive choice of the smoothing parameters...

In this paper we study stochastic processes which enable monitoring the possible changes of probability distributions over time. These so-called monitoring processes are bivariate functions of time and position at the measurement scale, and in particular be used to test the null hypothesis of no...

In this paper we study stochastic processes which enable monitoring the possible changes of probability distributions over time. These so-called monitoring processes are bivariate functions of time and position at the measurement scale, and in particular be used to test the null hypothesis of no...

Additive modelling has been widely used in nonparametric regression to circumvent the "curse of dimensionality", by reducing the problem of estimating a multivariate regression function to the estimation of its univariate components. Estimation of these univariate functions, however, can suffer...

Additive modelling is known to be useful for multivariate nonparametric regression as it reduces the complexity of problem to the level of univariate regression. This usefulness could be compromised if the data set was contaminated by outliers whose detection and removal are particularly...

This paper develops univariate seasonal unit root tests based on spectral regression estimators. An advantage of the frequency domain approach is that it enables serial correlation to be treated non-parametrically. We demonstrate that our proposed statistics have pivotal limiting distributions...