One of the main applications of statistical smoothing techniques is nonparametric regression. For the last 15 years there has been a strong theoretical interest in the development of such techniques. Related algorithmic concepts have been a main concern in computational statistics. Smoothing techniques in regression as well as other statistical methods are increasingly applied in biosciences and economics. But they are also relevant for medical and psychological research. Introduced are new developments in scatterplot smoothing and applications in statistical modelling. The treatment of the topics is on an intermediate level avoiding too much technicalities. Computational and applied aspects are considered throughout. Of particular interest to readers is the discussion of recent local fitting techniques.
Inhaltsverzeichnis
1 A Personal View of Smoothing and Statistics. - 2 Smoothing by Local Regression: Principles and Methods. - 3 Variance Properties of Local Polynomials and Ensuing Modifications. - 4 Comments. - 5 Comments. - 6 Comments. - 7 Comments. - 8 Rejoinder. - 9 Rejoinder. - 10 Rejoinder. - 11 Robust Bayesian Nonparametric Regression. - 12 The Invariance of Statistical Analyses with Smoothing Splines with Respect to the Inner Product in the Reproducing Kernel Hilbert Space. - 13 A Note on Cross Validation for Smoothing Splines. - 14 Some Comments on Cross-Validation. - 15 Extreme Percentile Regression. - 16 Mean and Dispersion Additive Models. - 17 Interaction in Nonlinear Principal Components Analysis. - 18 Nonparametric Estimation of Additive Separable Regression Models.
