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."
"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."
"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