Maximum Penalized Likelihood Estimation

Volume II: Regression

Paul P. Eggermont, Vincent N. LaRiccia

$508.95 $406.76

Hardback

Not in-store but you can order this
How long will it take?

Availability Information

We source books from suppliers in Australia and overseas. For books we don't currently have in stock, the time it takes to get them from our suppliers can vary widely - from a few days to a few months - so we check each book with each supplier to determine the expected time it will take to be supplied to us.

We then advise you accordingly. If the time taken to get any book is too long for you, you can let us know and we will cancel or adjust your order, and refund as required.

To find out the anticipated arrival time for specific items prior to ordering, please contact us by phone or email:

Phone +61 2 9264 3111, or 1800 4 BOOKS (1800 4 26657) if outside Sydney:
option 1 Abbey's Bookshop (Crime, History, Science, Kids & more) • info@abbeys.com.au
option 2 Language Book Centre (ESL & Foreign Languages) • language@abbeys.com.au
option 3 Galaxy Bookshop (Sci-fi, Fantasy, Romance, Graphic Novels) • sf@galaxybooks.com.au

QTY:

English

Springer-Verlag New York Inc.
06 July 2009

Econometrics; Probability & statistics; Stochastics; Electronics engineering; Pattern recognition

Series: Springer Series in Statistics

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.

By: Paul P. Eggermont, Vincent N. LaRiccia
Imprint: Springer-Verlag New York Inc.
Country of Publication: United States
Edition: 2009 ed.
Dimensions: Height: 235mm, Width: 155mm, Spine: 31mm
Weight: 2.210kg
ISBN: 9780387402673
ISBN 10: 0387402675
Series: Springer Series in Statistics
Pages: 572
Publication Date: 06 July 2009
Audience: Professional and scholarly , Undergraduate
Format: Hardback
Publisher's Status: Active

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.

Reviews for Maximum Penalized Likelihood Estimation: Volume II: Regression

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)