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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.
List of contents
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.
About the author
Wolfgang Härdle is a professor of statistics at the Humboldt-Universität zu Berlin and director of C.A.S.E. the Centre for Applied Statistics and Economics. He teaches quantitative finance and semiparametric statistical methods. His research focuses on dynamic factor models, multivariate statistics in finance and computational statistics. He is an elected ISI member and advisor to the Guanghua School of Management, Peking University and to National Central University, Taiwan.
Michael G. Schimek, PhD, DPhil, is Professor of Statistics and Biometrics in the Department of Medical Informatics, Statistics, and Documentation at Karl-Franzens-University of Graz, Austria, and Adjunct Professor of Methodology in the Department of Psychology at the University of Vienna, Austria.
