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.
Rezensionen / Stimmen
From the reviews:
"This book is meant for specialized readers or graduate students interested in the theory, computation and application of Nonparametric Regression to real data, and the new contributions of the authors. . For mathematically mature readers, the book would be a delight to read. . The authors have not only written a scholarly and very readable book but provide major new methods and insights. . it would help evaluate the methods as well as lead to teachable notes for a graduate course." (Jayanta K. Ghosh, International Statistical Review, Vol. 79 (1), 2011)
"This book is the second volume of a three-volume textbook in the Springer Series in Statistics. . The second volume also belongs to the literature on nonparametric statistical inference and concentrates mainly on nonparametric regression. . The book can be used for two main purposes: as a textbook for M.S./Ph.D. students in statistics, operations research, and applied mathematics, and as a tool for researchers and practitioners in these fields who want to develop and to apply nonparametric regression methods." (Yurij S. Kharin, Mathematical Reviews, Issue 2012 g)
