Generalized Additive Models

Generalized Additive Models (1995)

by Trevor Hastie , Robert Tibshirani

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Datum	Value	Source
TITLE	Generalized Additive Models	user correction - Legacy Corrections
AUTHOR NAME	Trevor Hastie	SVM HeaderParse 0.1
AUTHOR NAME	Robert Tibshirani	SVM HeaderParse 0.1
AUTHOR AFFIL	; z; Department of Statistics and Division of Biostatistics; Stanford University; = ff + fi 1 x 1 +: : : + fi p x p (1); y; Department of Statistics, Sequoia Hall, Stanford University, Stanford California 94305;	SVM HeaderParse 0.2
AUTHOR ADDR	; 1 \Gamma	SVM HeaderParse 0.1
ABSTRACT	This article describes flexible statistical methods that may be used to identify and characterize nonlinear regression effects. These methods are called "generalized additive models". For example, a commonly used statistical model in medical research is the logistic regression model for binary data. Here we relate the mean of the binary response ¯ = P (y = 1) to the predictors via a linear regression model and the logit link function: log	user correction - Legacy Corrections
YEAR	1995	user correction - Legacy Corrections
CITATIONS	14 found	ParsCit 1.0