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7+ YearsPublisher | Springer-Verlag New York Inc. |
ISBN 13 | 9780387402673 |
Author | P.P.B. Eggermont, V.N. Lariccia |
Language | English |
Book Description | Unique blend of asymptotic theory and small sample practice through simulation experiments and data analysis.Novel reproducing kernel Hilbert space methods for the analysis of smoothing splines and local polynomials. Leading to uniform error bounds and honest confidence bands for the mean function using smoothing splines.Exhaustive exposition of algorithms, including the Kalman filter, for the computation of smoothing splines of arbitrary order. |
Editorial Review | “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) |
Number of Pages | 572 |
Maximum Penalized Likelihood Estimation Hardcover English by P.P.B. Eggermont