Showing papers by "Jerome H. Friedman published in 1984"
TL;DR: A variable span smoother based onlinear fits based on linear fits is described, and Computationally efficient algorithms making use of updating formulas are presented.
Abstract: : A variable span smoother based on linear fits is described. Local cross-validation is used to estimate the optimal span as a function of abscissa value. Computationally efficient algorithms making use of updating formulas are presented.
439 citations
TL;DR: In this article, the projection pursuit methodology is applied to the multivariate density estimation problem and the resulting nonparametric procedure is often less biased than the kernel and near-neighbor methods.
Abstract: The projection pursuit methodology is applied to the multivariate density estimation problem. The resulting nonparametric procedure is often less biased than the kernel and near-neighbor methods. In addition, graphical information is produced that can be used to help gain geometric insight into the multivariate data distribution.
240 citations
TL;DR: In this article, a solution that combines local averaging and isotonic regression is proposed to summarize a scatterplot with a smooth, monotone curve, and it is shown how to generalize Box and Cox's well-known family of transformations.
Abstract: We consider the problem of summarizing a scatterplot with a smooth, monotone curve. A solution that combines local averaging and isotonic regression is proposed, and we demonstrate its use with two examples. In the second example, the procedure is applied, in a regression setting, to some data from Box and Cox (1964), and it is shown how this new procedure generalizes Box and Cox's well-known family of transformations. In the same example, the bootstrap is applied to obtain a measure of the variability of the procedure.
152 citations
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01 Oct 1984
TL;DR: Software implementing the SMART (Smooth Multiple Additive Regression Technique) algorithm, which generalizes the projection pursuit method to classification and multiple response regression, is described.
Abstract: : This note describes software implementing the SMART(Smooth Multiple Additive Regression Technique) algorithm. SMART generalizes the projection pursuit method to classification and multiple response regression. SMART also provides a more efficient algorithm for single response projection pursuit regression. Originator-supplied keywords include: Multiple response regression, Non parametric regression, Classification, and Discriminant analysis.
61 citations