Artikelomschrijving

Kernel Smoothing explores both the theory and methods of kernel smoothing as well as introducing the fundamental methods and principles of related topics in parametric statistical inference. It addresses polynomials, with uncorrelated or i.i.d. observations for univariate and multivariate density estimation and nonparametric regression, whilst kernel based curve estimation methods are introd uced alongside a study of their theoretical properties and optimality issues. Furthermore, this book looks at temporal and spatial correlations in data for which cases occur frequently in particular in the natural sciences. Addressing a variety of models, it offers a 'user-friendly' presentation of the mathematical content so that the reader can also directly implement the formulas using any appropriate software.