Multiple and generalized nonparametric regression

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.

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[目次]

  • Local Polynomial Multiple Regression Additive Regression Models Projection-Pursuit Regression Regression Trees Generalized Nonparametric Regression Concluding Remarks Integrating Nonparametric Regression in Statistical Practice

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この本の情報

書名 Multiple and generalized nonparametric regression
著作者等 Fox, John
シリーズ名 Sage university papers series
出版元 Sage Publications
刊行年月 c2000
ページ数 vii, 83 p.
大きさ 22 cm
ISBN 9780761921899
NCID BA47296527
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言語 英語
出版国 アメリカ合衆国
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