Accession Number : ADA181773
Title : Generalized Additive Models, Cubic Splines and Penalized Likelihood.
Descriptive Note : Technical rept.,
Corporate Author : STANFORD UNIV CA DEPT OF STATISTICS
Personal Author(s) : Hastie,Trevor ; Tibshirani,Robert
PDF Url : ADA181773
Report Date : 22 May 1987
Pagination or Media Count : 23
Abstract : Generalized additive models extended the class of generalized linear models by allowing an arbitrary smooth function for any or all of the covariates. The functions are established by the local scoring procedure, using a smoother as a building block in an iterative algorithm. This paper utilizes a cubic spline smoother in the algorithm and show how the resultant procedure can be view as a method for automatically smoothing a suitably defined partial residual, and more formally, a method for maximizing a penalized likelihood. The authors also examine convergence of the inner (backfitting) loop in this case and illustrate these ideas with some binary response data. Keywords: Spline; Non-parametric regression.
Descriptors : *NONPARAMETRIC STATISTICS, *BIOSTATISTICS, BINARY NOTATION, CUBIC SPLINE TECHNIQUE, ALGORITHMS, ITERATIONS, SCORING, LINEARITY, MATHEMATICAL MODELS, REGRESSION ANALYSIS, ADDITION, FORTRAN
Subject Categories : Statistics and Probability
Medicine and Medical Research
Distribution Statement : APPROVED FOR PUBLIC RELEASE