Ideal for researchers and practitioners in statistics and industrial mathematics, this book covers the theory and practice of nonparametric estimation. It is novel in its use of maximum penalized likelihood estimation and convex minimization problem theory.

This book is intended for graduate students in statistics and industrial mathematics, as well as researchers and practitioners in the field. It covers both theory and practice of nonparametric estimation. The text is novel in its use of maximum penalized likelihood estimation, and the theory of convex minimization problems (fully developed in the text) to obtain convergence rates. A substantial effort has been made to discuss computational details, and to include simulation studies and analyses of some classical data sets using fully automatic (data driven) procedures.

Fully develops the theory of convex minimization problems to obtain convergence rates Includes simulation studies and analyses of classical data sets using fully automatic (data driven) procedures Many topics appear for the first time in textbook form Intended for graduate students as well as researchers and practitioners in the field of statistics and industrial mathematics Includes supplementary material: sn.pub/extras

Inhalt
Nonparametric Regression.- Smoothing Splines.- Kernel Estimators.- Sieves.- Local Polynomial Estimators.- Other Nonparametric Regression Problems.- Smoothing Parameter Selection.- Computing Nonparametric Estimators.- Kalman Filtering for Spline Smoothing.- Equivalent Kernels for Smoothing Splines.- Strong Approximation and Confidence Bands.- Nonparametric Regression in Action.