Volume 64. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. Clean from markings. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,500grams, ISBN:9780412047213.

Bestandsnummer des Verk�ufers 9878124

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Bibliografische Details

Titel

Monographs on Statistics and Applied Probability: Measurement Error in Nonlinear Models (Volume 64)

Verlag

Chapman & Hall

Ver�ffentlichungsdatum

1995

Sprache

Englisch

ISBN-10

0412047217

ISBN-13

9780412047213

Bindung

Hardcover

Zustand

Good

Verk�uferkataloge

Mathematics

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Inhaltsangabe

This monograph provides an up-to-date discussion of analysis strategies for regression problems in which predictor variables are measured with errors. The analysis of nonlinear regression models includes generalized linear models, transform-both-sides models and quasilikelihood and variance function problems. The text concentrates on the general ideas and strategies of estimation and inference rather than being concerned with a specific problem. Measurement error occurs in many fields, such as biometry, epidemiology and economics. In particular, the book contains a large number of epidemiological examples. An outline of strategies for handling progressively more difficult problems is also provided.

Cr�ticas

"I enjoyed reading this book and would recommend it to statisticians for their library." -Journal of Quality Technology ..."This present book is an essential purchase for the library and an excellent purchase for individuals." -International Statistical Institute ..."the book provides an up-to-date introduction to the application of MCMC methods. It is both readable and coherent and should form an essential addition to any modern applied statistician." -The Statistician "It has been a pleasure reading this book...the range of the authors appreciation of the actual issues and challenges pertaining to particular data sets, and extensive theoretical justification of estimation techniques is very impressive." -Biometrics

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