A Uniform Central Limit Theorem and Efficiency for Deconvolution Estimators

A Uniform Central Limit Theorem and Efficiency for Deconvolution Estimators
Author :
Publisher :
Total Pages :
Release :
ISBN-10 : OCLC:1155608075
ISBN-13 :
Rating : 4/5 ( Downloads)

Book Synopsis A Uniform Central Limit Theorem and Efficiency for Deconvolution Estimators by : Jakob Söhl

Download or read book A Uniform Central Limit Theorem and Efficiency for Deconvolution Estimators written by Jakob Söhl and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:


A Uniform Central Limit Theorem and Efficiency for Deconvolution Estimators Related Books

A Uniform Central Limit Theorem and Efficiency for Deconvolution Estimators
Language: en
Pages:
Authors: Jakob Söhl
Categories:
Type: BOOK - Published: 2012 - Publisher:

DOWNLOAD EBOOK

Uniform Central Limit Theorems
Language: en
Pages: 485
Authors: R. M. Dudley
Categories: Mathematics
Type: BOOK - Published: 2014-02-24 - Publisher: Cambridge University Press

DOWNLOAD EBOOK

This expanded edition of the classic work on empirical processes now boasts several new proved theorems not in the first.
Uniform Central Limit Theorems
Language: en
Pages: 485
Authors: R. M. Dudley
Categories: Mathematics
Type: BOOK - Published: 2014-02-24 - Publisher: Cambridge University Press

DOWNLOAD EBOOK

In this new edition of a classic work on empirical processes the author, an acknowledged expert, gives a thorough treatment of the subject with the addition of
Uniform Central Limit Theorems
Language: en
Pages: 482
Authors: R. M. Dudley
Categories: Central limit theorem
Type: BOOK - Published: 2014 - Publisher:

DOWNLOAD EBOOK

Mathematical Foundations of Infinite-Dimensional Statistical Models
Language: en
Pages: 706
Authors: Evarist Giné
Categories: Mathematics
Type: BOOK - Published: 2021-03-25 - Publisher: Cambridge University Press

DOWNLOAD EBOOK

In nonparametric and high-dimensional statistical models, the classical Gauss–Fisher–Le Cam theory of the optimality of maximum likelihood estimators and Ba