Applied Nonparametric Regression

Rating:
Summary: Very clear and readable account of nonparametric regression
Review: This book deals with nonparametric regression, mainly kernel regression, though other estimators are also covered. This is a very clear and succinct discussion of the theory and application of this very important area of modern statistics. This book is one of the first few books on the subject (I saw a preprint of this book while a graduate student and bought a copy when it first appeared). Kernel estimators are known to be less efficient and suffer from boundary effects when the underlying regression is very smooth (twice differentiable). Local polynomial regression is the natural remedy and has experienced rapid developments in last 10 years. Fan and Gijbel's book in 1996 has detailed discussions on local polynomial estimation, an area which I also publish a few papers. But this book still contains substantial materials not found elsewhere. For example, this book has excellent discussion of nonparametric methods in econometric applications. I highly recommend it for its good introduction and as a valuable reference book on this subject.