Logo - springer
Slogan - springer

Mathematics - Probability Theory and Stochastic Processes | Maximum Penalized Likelihood Estimation - Volume II: Regression

Maximum Penalized Likelihood Estimation

Volume II: Regression

Eggermont, Paul P., LaRiccia, Vincent N.

2009

Available Formats:
eBook
Information

Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.

You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.

After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.

 
$159.00

(net) price for USA

ISBN 978-0-387-68902-9

digitally watermarked, no DRM

Included Format: PDF

download immediately after purchase


learn more about Springer eBooks

add to marked items

Hardcover
Information

Hardcover version

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.

 
$199.00

(net) price for USA

ISBN 978-0-387-40267-3

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days


add to marked items

Softcover
Information

Softcover (also known as softback) version.

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.

 
$199.00

(net) price for USA

ISBN 978-1-4614-1712-5

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days


add to marked items

  • Fully develops the theory of convex minimization problems to obtain convergence rates
  • Includes simulation studies and analyses of classical data sets using fully automatic (data driven) procedures
  • Many topics appear for the first time in textbook form
  • Intended for graduate students as well as researchers and practitioners in the field of statistics and industrial mathematics

This is the second volume of a text on the theory and practice of maximum penalized likelihood estimation. It is intended for graduate students in statistics, operations research and applied mathematics, as well as for researchers and practitioners in the field. The present volume deals with nonparametric regression.

The emphasis in this volume is on smoothing splines of arbitrary order, but other estimators (kernels, local and global polynomials) pass review as well. Smoothing splines and local polynomials are studied in the context of reproducing kernel Hilbert spaces. The connection between smoothing splines and reproducing kernels is of course well-known. The new twist is that letting the innerproduct depend on the smoothing parameter opens up new possibilities. It leads to asymptotically equivalent reproducing kernel estimators (without qualifications), and thence, via uniform error bounds for kernel estimators, to uniform error bounds for smoothing splines and via strong approximations, to confidence bands for the unknown regression function.

The reason for studying smoothing splines of arbitrary order is that one wants to use them for data analysis. Regarding the actual computation, the usual scheme based on spline interpolation is useful for cubic smoothing splines only. For splines of arbitrary order, the Kalman filter is the most important method, the intricacies of which are explained in full. The authors also discuss simulation results for smoothing splines and local and global polynomials for a variety of test problems as well as results on confidence bands for the unknown regression function based on undersmoothed quintic smoothing splines with remarkably good coverage probabilities.

P.P.B. Eggermont and V.N. LaRiccia are with the Statistics Program of the Department of Food and Resource Economics in the College of Agriculture and Natural Resources at the University of Delaware, and the authors of Maximum Penalized Likelihood Estimation: Volume I: Density Estimation.

Content Level » Research

Keywords » Confidence bands - Estimator - Kalman filter for smoothing splines. - Likelihood - Local polynomials - Nonparametric regression - Reproducing kernel Hilbert spaces - Smoothing splines - Uniform error bounds - data analysis - expectation–maximization algorithm

Related subjects » Econometrics / Statistics - Image Processing - Probability Theory and Stochastic Processes - Signals & Communication - Statistical Theory and Methods

Table of contents / Preface / Sample pages 

Popular Content within this publication 

 

Articles

Read this Book on Springerlink

Services for this book

New Book Alert

Get alerted on new Springer publications in the subject area of Probability Theory and Stochastic Processes.

Additional information