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Cambridge Series in Statistical and Probabilistic Mathematics : Semiparametric Regression Series Number 12 - David Ruppert

Cambridge Series in Statistical and Probabilistic Mathematics

Semiparametric Regression Series Number 12

Hardcover Published: 5th September 2011
ISBN: 9780521780506
Number Of Pages: 404

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Semiparametric regression is concerned with the flexible incorporation of non-linear functional relationships in regression analyses. Any application area that benefits from regression analysis can also benefit from semiparametric regression. Assuming only a basic familiarity with ordinary parametric regression, this user-friendly book explains the techniques and benefits of semiparametric regression in a concise and modular fashion. The authors make liberal use of graphics and examples plus case studies taken from environmental, financial, and other applications. They include practical advice on implementation and pointers to relevant software. The book is suitable as a textbook for students with little background in regression as well as a reference book for statistically oriented scientists such as biostatisticians, econometricians, quantitative social scientists, epidemiologists, with a good working knowledge of regression and the desire to begin using more flexible semiparametric models. Even experts on semiparametric regression should find something new here.

'I would recommend this book to anyone interested in the field. it is very readable, informative without being heavy, and (excellent news) comes in a paperback version as well as hardback.' Publication of the International Statistical Institute 'I would recommend this book to anyone interested in the field. It is very readable, informative without being heavy, and (excellent news) comes in a paperback version as well as hardback.' Short Book Reviews 'This great book is the first one to remove barriers and to close gaps between advanced statistical methodology and applied research in various fields ... I highly recommend this book ... It provides a very readable access to modern semiparametric regression, demonstrates its potential in various applications, and is an inspiring source for new ideas. I enjoyed reading this book.' L. Fahrmeir, Ludwig Maximilian University Biometrics '... contains clear presentations of new developments in the field and also the state of the art in classical methods ... I found it an easily readable book; its coverage of material was extensive and well explained and well illustrated ... I found the material useful and I recommend it strongly to anyone who is interested in modern nonparametric methods, whether they are expert or not ... But here are 500-odd pages of good teaching material, nicely done, culminating in the arc-sine law and the Black-Scholes formula: anyone teaching probability would be glad to have it to hand.' The Journal of the Royal Statistical Society 'This book provides an extensive overview of techniques for semiparametric regression ... I think it may be very useful for a more practically oriented audience.' Kwantitatieve Methoden '... an easily readable book; its coverage of material was extensive and well explained and well illustrated ... I recommend it strongly to anyone who is interested in modern nonparametric methods, whether they are expert or not.' Marian Scott, University of Glasgow Journal of the Royal Statistical Society '... provides a great overview of semiparametric regression and it is a useful guide to practical semiparametric analyses using standard statistical software.' Metrika 'This book is a very nice book for data analysis and indicates how to flexibly develop and analyze complex models using penalized spline functions. The examples are nontrivial and very useful, but there are no attempts to develop an asymptotic theory.' Mathematical Reviews

Introduction
Parametric regression
Scatterplot smoothing
Mixed models
Automatic scatterplot smoothing
Inference
Simple semiparametric models
Additive models
Semiparametric mixed models
Generalized parametric regression
Generalized additive models
Interaction models
Bivariate smoothing
Variance function estimation
Measurement error
Bayesian semiparametric regression
Spatially adaptive smoothing
Analyses
Epilogue
Technical complements
Computational issues
Table of Contents provided by Publisher. All Rights Reserved.

ISBN: 9780521780506
ISBN-10: 0521780500
Series: Cambridge Series in Statistical and Probabilistic Mathematics
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 404
Published: 5th September 2011
Publisher: Cambridge University Press
Country of Publication: GB
Dimensions (cm): 25.4 x 17.8  x 2.4
Weight (kg): 0.92