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Semiparametric Regression

Semiparametric Regression

$55.99 (P)

Part of Cambridge Series in Statistical and Probabilistic Mathematics

  • Date Published: July 2003
  • availability: Available
  • format: Paperback
  • isbn: 9780521785167

$ 55.99 (P)

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About the Authors
  • Science abounds with problems where the data are noisy and the answer is not a straight line. Semiparametric regression analysis helps make sense of such data in application areas that include engineering, finance, medicine and public health. The book is geared towards researchers and professionals with little background in regression as well as statistically oriented scientists (biostatisticians, econometricians, quantitative social scientists, and epidemiologists) with knowledge of regression and the desire to begin using more flexible semiparametric models. Author resource page:

    • 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
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    Reviews & endorsements

    "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."

    "… 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

    "Although appealing to statistically-oriented scientists, this book also should not fail to attract the attention of experts in the field, because it provides a fresh perspective on smoothing and addresses ongoing computational and theoretical issues. The text is quite comprehensive, and the chapters are carefully organized for a coherent development of the subject...this is a book that I would strongly recommend to practitioners who want to learn nonparametric regression techniques and apply them to their own problems without being burdened by advanced mathematical concepts such as a reproducing kernel Hilbert space."
    Yoonkyung Lee, Journal of the American Statistical Association

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    Product details

    • Date Published: July 2003
    • format: Paperback
    • isbn: 9780521785167
    • length: 404 pages
    • dimensions: 253 x 178 x 19 mm
    • weight: 0.7kg
    • contains: 82 b/w illus.
    • availability: Available
  • 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 smoothing
    18. Analyses
    19. Epilogue
    A. Technical complements
    B. Computational issues.

  • Authors

    David Ruppert, Cornell University, New York

    M. P. Wand, University of Technology, Sydney

    R. J. Carroll, Texas A & M University

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