Logo - springer
Slogan - springer

Statistics - Statistical Theory and Methods | Maximum Penalized Likelihood Estimation - Volume I: Density Estimation

Maximum Penalized Likelihood Estimation

Volume I: Density Estimation

Eggermont, P.P.B., LaRiccia, V.N.

2001, XVIII, 512 p.

Available Formats:
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.

 
$179.00

(net) price for USA

ISBN 978-0-387-95268-0

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.

 
$179.00

(net) price for USA

ISBN 978-1-4419-2928-0

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days


add to marked items

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.

Content Level » Research

Keywords » Density Estimation - Maximum Likelihood - Maximum Penalized Likelihood

Related subjects » Operations Research & Decision Theory - Statistical Theory and Methods

Table of contents / Preface / Sample pages 

Parametric Maximum Likelihood Estimation * Parametric Maximum Likelihood Estimation in Action * Kernel Density Estimation * Maximum Likelihood Density Estimation * Monotone and Unimodal Densities * Choosing the Smoothing Parameter * Nonparametric Density Estimation in Action * Convex Minimization in Finite Dimensional Spaces * Convex Minimization in Infinite Dimensional Spaces * Convexity in Action

Popular Content within this publication 

 

Articles

Services for this book

New Book Alert

Get alerted on new Springer publications in the subject area of Statistical Theory and Methods.

Additional information