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Semiparametric Regression
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Details

  • 82 b/w illus.
  • Page extent: 404 pages
  • Size: 253 x 177 mm
  • Weight: 0.7 kg
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Paperback

 (ISBN-13: 9780521785167 | ISBN-10: 0521785162)

DOI: 10.2277/0521785162

Manufactured on demand: supplied direct from the printer

 (Stock level updated: 01:56 GMT, 05 September 2015)

£39.99

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 2003 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.

• Focus on penalized regression splines as a type of linear mixed model (or generalized linear mixed model (GLMMs) for non-Gaussian response • Appeals to a broad and diverse audience • Many examples and case studies; liberal use of graphics • Concise, modular and user-friendly format

Contents

1. Introduction; 2. Parametric regression; 3. Scatterplot smoothing; 4. Mixed models; 5. Automatic scatterplot smoothing; 6. Inference; 7. Simple semiparametric models; 8. Additive models; 9. Semiparametric mixed models; 10. Generalized parametric regression; 11. Generalized additive models; 12. Interaction models; 13. Bivariate smoothing; 14. Variance function estimation; 15. Measurement error; 16. Bayesian semiparametric regression; 17. Spatially adaptive smoothing; 18. Analyses; 19. Epilogue; A. Technical complements; B. Computational issues.

Reviews

'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

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