Nonparametric Simple Regression: Smoothing Scatterplots

by John Fox

Overview

John Fox introduces readers to the techniques of kernel estimation, additive nonparametric regression, and the ways nonparametric regression can be employed to select transformations of the data preceding a linear least-squares fit.

Product Details

ISBN-13:	9780761915850
Publisher:	SAGE Publications
Publication date:	01/20/2000
Series:	Quantitative Applications in the Social Sciences , #130
Edition description:	New Edition
Pages:	96
Product dimensions:	5.50(w) x 8.50(h) x (d)

About the Author

John Fox received a BA from the City College of New York and a Ph D from the University of Michigan, both in Sociology. He is Professor Emeritus of Sociology at Mc Master University in Hamilton, Ontario, Canada, where he was previously the Senator William Mc Master Professor of Social Statistics. Prior to coming to Mc Master, he was Professor of Sociology, Professor of Mathematics and Statistics, and Coordinator of the Statistical Consulting Service at York University in Toronto. Professor Fox is the author of many articles and books on applied statistics, including \emph{Applied Regression Analysis and Generalized Linear Models, Third Edition} (Sage, 2016). He is an elected member of the R Foundation, an associate editor of the Journal of Statistical Software, a prior editor of R News and its successor the R Journal, and a prior editor of the Sage Quantitative Applications in the Social Sciences monograph series.

Table of Contents

What Is Nonparametric Regression?
Binning and Local Averaging
Kernel Estimation
Local Polynomial Regression
Splines
Nonparametric Regression and Data Analysis