Overview
This book is intended for graduate students in statistics and industrial mathematics, as well as researchers and practitioners in the field. We cover 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. We also use (and develop from an elementary view point) discrete parameter submartingales and exponential inequalities. 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. Some theoretical topics that appear in textbook form for the first time are definitive treatments of I.J. Good's roughness penalization, monotone and unimodal density estimation, asymptotic optimality of generalized cross validation for spline smoothing and analogous methods for ill-posed least squares problems, and convergence proofs of EM algorithms for random sampling problems.
Full Product Details
Author: Paul P. Eggermont ,
Vincent N. LaRiccia
Publisher: Springer-Verlag New York Inc.
Imprint: Springer-Verlag New York Inc.
Edition: 2009 ed.
Dimensions:
Width: 15.50cm
, Height: 3.10cm
, Length: 23.50cm
Weight: 2.210kg
ISBN: 9780387402673
ISBN 10: 0387402675
Pages: 572
Publication Date: 06 July 2009
Audience:
Professional and scholarly
,
Professional & Vocational
Format: Hardback
Publisher's Status: Active
Availability: In Print
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Reviews
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)
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)
