In this paper we discuss estimation and diagnostic procedures in semiparametric additive models with symmetric errors in order to permit distributions with heavier and lighter tails than the normal ones, such as Student-t, Pearson VII, power exponential, logistics I and II, and contaminated...

Numerous studies have addressed the question of the econometric specification of the Environmental Kuznets Curve (EKC). This paper adds preliminary results on nonstationarity and its effect on functional form using a panel data set for the U.S. by state from 1929 to 1994. It is found that...

We propose a semi-parametric spatial auto-covariance specification of the growth model to examine the growth behaviour of European regions in the period 1988-2000. This specification simultaneously takes account of the problems of non-linearities and spatial dependence. We obtain two main...

The similarity between objects is a fundamental element of many learning algorithms. Most non-parametric methods take this similarity to be fixed, but much recent work has shown the advantages of learning it, in particular to exploit the local invariances in the data or to capture the possibly...

In this paper, we study and put under a common framework a number of non-linear dimensionality reduction methods, such as Locally Linear Embedding, Isomap, Laplacian Eigenmaps and kernel PCA, which are based on performing an eigen-decomposition (hence the name 'spectral'). That framework also...

We describe an interesting application of the principle of local learning to density estimation. Locally weighted fitting of a Gaussian with a regularized full covariance matrix yields a density estimator which displays improved behavior in the case where much of the probability mass is...