Advanced text; estimation methods in statistics, e.g. least squares; lots of examples; minimal abstraction.
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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.
'... well written and provides a modern contribution to a very important class of nonparametric estimators.' N. D. C. Veraverbeke, Publication of the International Statistical Institute
'... this excellent book will be extremely useful for graduate students and researchers in the general area of nonparametric estimation. It is a welcome addition to the existing literature and certainly recommended.' Niew Archief voor Wiskunde
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Vendeur : Studibuch, Stuttgart, Allemagne
hardcover. Etat : Gut. 286 Seiten; 9780521650021.3 Gewicht in Gramm: 1. N° de réf. du vendeur 1076803
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Vendeur : BUCHSERVICE / ANTIQUARIAT Lars Lutzer, Wahlstedt, Allemagne
Etat : gut. 2000. Applications of Empirical Process Theory (Cambridge Series in Statistical and Probabilistic Mathematics, 6) In englischer Sprache. pages. N° de réf. du vendeur BN336457
Quantité disponible : 1 disponible(s)