Applied Nonparametric Regression

Applied Nonparametric Regression

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Applied Nonparametric Regression is the first book to bring together in one place the techniques for regression curve smoothing involving more than one variable. The computer and the development of interactive graphics programs have made curve estimation possible. This volume focuses on the applications and practical problems of two central aspects of curve smoothing: the choice of smoothing parameters and the construction of confidence bounds. Hardle argues that all smoothing methods are based on a local averaging mechanism and can be seen as essentially equivalent to kernel smoothing. To simplify the exposition, kernel smoothers are introduced and discussed in great detail. Building on this exposition, various other smoothing methods (among them splines and orthogonal polynomials) are presented and their merits discussed. All the methods presented can be understood on an intuitive level; however, exercises and supplemental materials are provided for those readers desiring a deeper understanding of the techniques. The methods covered in this text have numerous applications in many areas using statistical analysis. Examples are drawn from economics as well as from other disciplines including medicine and more

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"Professor Hardle has provided us with an important book, one that will be appreciated both by applied statisticians who want to implement nonparametric regression techniques and by theoreticians interested in becoming knowledgeable in this growing field. Applied Nonparametric Regression is a very welcome addition to the literature." Journal of the American Statistical Association "Nonparametric regression analysis has become central to economic theory. Hardle, by writing the first comprehensive and accessible book on the subject, has contributed enormously to making nonparametric regression equally central to econometric practice." Charles F. Manski, University of Wisconsin, Madison "This book represents an optimally estimated common thread for the numerous topics and results in the fast-growing area of nonparametric regression. The user-friendly approach taken by the author has successfully smoothed out most of the formidable asymptotic elaboration in developing the theory. This is an excellent collection for both beginners and experts." Ker-Chau Li, University of California, Los Angeles "This monograph on nonparametric regression presents a particularly clear and balanced view of the methodology and practice of this very important subject, and so is of use to theoreticians and practitioners alike." Peter Hall, University of Glasgow "This book makes the main ideas and methodologies of nonparametric regression easily accessible to nonexperts, and is a valuable reference source for experts as well because of its wide scope." J.S. Marron "...Hardle has written an important book on NPR that will undoubtedly serve as one of the standards in this field for some time to come." R. L. Eubank, Technometricsshow more

Table of contents

Preface; Part I. Regression Smoothing: 1. Introduction; 2. Basic idea of smoothing 3. Smoothing techniques; Part II. The Kernel Method: 4. How close is the smooth to the true curve?; 5. Choosing the smoothing parameter; 6. Data sets with outliers; 7. Smoothing with correlated data; 8. Looking for special features (qualitative smoothing); 9. Incorporating parametric components and alternatives; Part III. Smoothing in High Dimensions: 10. Investigating multiple regression by additive models; Appendices; References; List of symbols and more