Showing papers by "Jerome H. Friedman published in 1981"

Projection Pursuit Regression

Abstract: A new method for nonparametric multiple regression is presented. The procedure models the regression surface as a sum of general smooth functions of linear combinations of the predictor variables in an iterative manner. It is more general than standard stepwise and stagewise regression procedures, does not require the definition of a metric in the predictor space, and lends itself to graphical interpretation.

Graphics for the Multivariate Two-Sample Problem

Abstract: Some graphical methods for comparing multivariate samples are presented. These methods are based on minimal spanning tree techniques developed for multivariate two-sample tests. The utility of these methods is illustrated through examples using both real and artificial data.

A Nested Partitioning Procedure for Numerical Multiple Integration

Abstract: : An algorithm is presented for adaptively partitioning a multidimensional coordinate space based on optimization of a scalar function of the coordinates. The goal is to construct a set of hyperrectangular regions, such that the variation of function values within each region is small. These regions are then used as the basis for a stratified sampling estimate of the definite integral of the function. (Author)

Hardware for kinematic statistical graphics.

Abstract: : The hardware requirements for a computer graphics system capable of supporting kinematic statistical graphics are specified. The various options are discussed, and the ORION-1 workstation currently in use at the Stanford Linear Accelerator Center is described. (Author)

An Adaptive Importance Sampling Procedure.

Abstract: : Monte Carlo calculations often require generation of a random sample of n-dimensional points drawn from a specified multivariate probability distribution. We present an importance sampling technique that can often greatly improve the efficiency of an acceptance/rejection generating method. The importance sampling function is defined as piecewise constant on a set of subregions, which are obtained by adaptively partitioning the sampling region so that the variation of density values within each subregion is relatively small. The partitioning strategy is based on multiparameter optimization to estimate the maximum and minimum of the original density function in each subregion. (Author)