Maximum Penalized Likelihood Estimation Volume II: Regression /

• Main Authors: LaRiccia, Vincent N. (Author), Eggermont, Paul P. (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: New York, NY : Springer New York, 2009.
• Series: Springer Series in Statistics,
• Subjects: Mathematics.; Biometrics (Biology).; Biostatistics.; Probabilities.; Statistics.; Econometrics.; Probability Theory and Stochastic Processes.; Statistical Theory and Methods.; Biometrics.; Signal, Image and Speech Processing.

This is the second volume of a text on the theory and practice of maximum penalized likelihood estimation. It is intended for graduate students in statistics, operations research and applied mathematics, as well as for researchers and practitioners in the field. The present volume deals with nonpara...

Table of Contents:

• 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.