Description: Semiparametric Regression This user-friendly 2003 book explains the techniques and benefits of semiparametric regression in a concise and modular fashion. David Ruppert (Author), M. P. Wand (Author), R. J. Carroll (Author) 9780521785167, Cambridge University Press Paperback / softback, published 14 July 2003 404 pages 25.3 x 17.8 x 1.9 cm, 0.7 kg '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 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. 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. Subject Areas: Probability & statistics [PBT], Mathematics [PB], Epidemiology & medical statistics [MBNS]
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BIC Subject Area 1: Probability & statistics [PBT]
BIC Subject Area 2: Mathematics [PB]
BIC Subject Area 3: Epidemiology & medical statistics [MBNS]
Number of Pages: 404 Pages
Publication Name: Semiparametric Regression
Language: English
Publisher: Cambridge University Press
Item Height: 253 mm
Subject: Mathematics, Healthcare System
Publication Year: 2003
Type: Textbook
Item Weight: 700 g
Author: R. J. Carroll, M. P. Wand, David Ruppert
Item Width: 178 mm
Series: Cambridge Series in Statistical and Probabilistic Mathematics
Format: Paperback
