Cambridge Series in Statistical and Probabilistic Mathematics: Semiparametric Regression Series Number 12 (Hardback)
  • Cambridge Series in Statistical and Probabilistic Mathematics: Semiparametric Regression Series Number 12 (Hardback)
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Cambridge Series in Statistical and Probabilistic Mathematics: Semiparametric Regression Series Number 12 (Hardback)

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£93.00
Hardback 404 Pages / Published: 14/07/2003
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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 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.

Publisher: Cambridge University Press
ISBN: 9780521780506
Number of pages: 404
Weight: 920 g
Dimensions: 254 x 178 x 24 mm


MEDIA 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
"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
"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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