Maximum penalized likelihood estimation. Volume I, Density estimation /

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 conve...

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Bibliographic Details
Main Authors: Eggermont, P. P. B., LaRiccia, V. N. (Author)
Format: eBook
Published: New York : Springer, 2001.
Series:Springer series in statistics,
Online Access:CONNECT
Table of Contents:
  • 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.