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Multiple and Generalized Nonparametric Regression by John Fox

Multiple and Generalized Nonparametric Regression

Part of the Quantitative Applications in the Social Sciences Series


Multiple and Generalized Nonparametric Regression by John Fox

While regression analysis traces the dependence of the distribution of a response variable to see if it bears a particular (linear) relationship to one or more of the predictors, nonparametric regression analysis makes minimal assumptions about the form of relationship between the average response and the predictors. This makes nonparametric regression a more useful technique for analyzing data in which there are several predictors that may combine additively to influence the response. (An example could be something like birth order/gender/and temperament on achievement motivation). Unfortunately, researchers have not had accessible information on nonparametric regression analysis-until now. Beginning with presentation of nonparametric regression based on dividing the data into bins and averaging the response values in each bin, 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. The book concludes with ways nonparametric regression can be generalized to logit, probit, and Poisson regression.

About the Author

John Fox is professor of sociology at McMaster University in Hamilton, Ontario, Canada. Fox earned a PhD in sociology from the University of Michigan in 1972, and prior to arriving at McMaster, he taught at the University of Alberta and at York University in Toronto, where he was cross-appointed in the sociology and mathematics and statistics departments and directed the university's statistical consulting service. He has delivered numerous lectures and workshops on statistical topics in North and South America, Europe, and Asia, at such places as the summer program of the Inter-University Consortium for Political and Social Research, the Oxford University Spring School in Quantitative Methods for Social Research, and the annual meetings of the American Sociological Association. Much of his recent work has been on formulating methods for visualizing complex statistical models and on developing software in the R statistical computing environment. He is the author and co-author of many articles, in such journals as Sociological Methodology, Sociological Methods and Research, The Journal of the American Statistical Association, The Journal of Statistical Software, The Journal of Computational and Graphical Statistics, Statistical Science, Social Psychology Quarterly, The Canadian Review of Sociology and Anthropology, and The Canadian Journal of Sociology. He has written a number of other books, including Regression Diagnostics (SAGE, 1991), Nonparametric Simple Regression (SAGE, 2000), Multiple and General-ized Nonparametric Regression (SAGE, 2000), A Mathematical Primer for Social Statistics (SAGE, 2008), and, with Sanford Weisberg, An R Companion to Applied Regression, Second Edition (SAGE, 2010). Fox also edits the SAGE Quantitative Applications in the Social Sciences (QASS) monograph series.

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Book Info

Publication date

19th July 2000


John Fox

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SAGE Publications Inc


96 pages


Social research & statistics



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