Synopses & Reviews
This user-friendly book explains the techniques and benefits of semiparametric regression in a concise and modular fashion.
Review
'\"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.\"
ISI 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.\"
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 ... 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.\"
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\"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.\"
Mathematical Reviews'
Synopsis
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.
Synopsis
Science abounds with problems where the data are noisy and the answer is not a straight line. Semiparametric regression aims to make sense of such data. Application areas include engineering, finance, medicine and public health. Semiparametric Regression explains this topic in a concise and modular fashion. The book is pitched towards researchers and professionals with little background in regression and 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.
Table of 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 splines; 18. Analyses of case studies; 19. Epilogue; A. Matrix and linear algebra; B. Vector differential equations; C. Useful results from probability theory; D. Theory for penalized splines; E. Computational issues.