Description: Maximum Penalized Likelihood Estimation : Regression, Paperback by Eggermont, Paul P.; Lariccia, Vincent N., ISBN 1461417120, ISBN-13 9781461417125, Brand New, Free shipping in the US 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.
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Book Title: Maximum Penalized Likelihood Estimation : Regression
Number of Pages: Xx, 572 Pages
Language: English
Publication Name: Maximum Penalized Likelihood Estimation : Volume II: Regression
Publisher: Springer New York
Publication Year: 2011
Subject: Probability & Statistics / General, Econometrics, Computer Vision & Pattern Recognition
Type: Textbook
Item Weight: 31.6 Oz
Subject Area: Mathematics, Computers, Business & Economics
Item Length: 9.3 in
Author: Vincent N. Lariccia, Paul P. Eggermont
Series: Springer Series in Statistics Ser.
Item Width: 6.1 in
Format: Trade Paperback
