Empirical Processes in M-Estimation: 6 (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 6) - Image 1

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Cambridge University Press Empirical Processes in M-Estimation: 6 (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 6)

Name: Empirical Processes in M-Estimation: 6 (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 6)
Brand: Cambridge University Press
Price: 44.52 GBP
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Probability & Statistics

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Description

The theory of empirical processes provides valuable tools for the development of asymptotic theory in (nonparametric) statistical models, and makes possible the unified treatment of a number of them. This book reveals the relation between the asymptotic behaviour of M-estimators and the complexity of parameter space. Virtually all results are proved using only elementary ideas developed within the book; there is minimal recourse to abstract theoretical results. To make the results concrete, a detailed treatment is presented for two important examples of M-estimation, namely maximum likelihood and least squares. The theory also covers estimation methods using penalties and sieves. Many illustrative examples are given, including the Grenander estimator, estimation of functions of bounded variation, smoothing splines, partially linear models, mixture models and image analysis. Graduate students and professionals in statistics as well as those with an interest in applications, to such areas as econometrics, medical statistics, etc., will welcome this treatment.

Product Specifications

Brand: Cambridge University Press
Format: paperback
ASIN: 0521123259
Category: Books > Subjects > Science, Nature & Maths > Mathematics > Probability & Statistics
Domain: Amazon UK
Release Date: 19 November 2009
Listed Since: 29 July 2009

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Cambridge University Press Empirical Processes in M-Estimation: 6 (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 6)

About as cheap as it gets. The only time it was cheaper was 3 months ago.

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