FUNCT ESTIM DENS, REGRESS MODEL & PROCES

Pons Odile

ISBN 10: 9814343730 / ISBN 13: 9789814343732

Published by World Scientific, 2011

About this Item

FUNCT ESTIM DENS, REGRESS MODEL & PROCES | Pons Odile | Buch | Gebunden | Englisch | 2011 | World Scientific | EAN 9789814343732 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 107124644

Bibliographic Details

• Title: FUNCT ESTIM DENS, REGRESS MODEL & PROCES
• Publisher: World Scientific
• Publication Date: 2011
• ISBN 10: 9814343730
• ISBN 13: 9789814343732
• Binding: Buch
• Condition: Neu
• Dimensions: 235W x 157H x 16D millimeters 235 millimeters width by 157 millimeters height by 16 millimeters depth
• Weight: 462 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

This book presents a unified approach on nonparametric estimators for models of independent observations, jump processes and continuous processes. New estimators are defined and their limiting behavior is studied. From a practical point of view, the book expounds on the construction of estimators for functionals of processes and densities, and provides asymptotic expansions and optimality properties from smooth estimators.

It also presents new regular estimators for functionals of processes, compares histogram and kernel estimators, compares several new estimators for single-index models, and it examines the weak convergence of the estimators.

From the Back Cover

This book presents a unified approach on nonparametric estimators for models of independent observations, jump processes and continuous processes. New estimators are defined and their limiting behavior is studied. From a practical point of view, the book expounds on the construction of estimators for functionals of processes and densities, and provides asymptotic expansions and optimality properties from smooth estimators.

It also presents new regular estimators for functionals of processes, compares histogram and kernel estimators of several new estimators for single-index models, and examines the weak convergence of the estimators.