A parallel solver for generalised additive models
type: Item
instance of: scholarly article
title: A parallel solver for generalised additive models
zbMATH Open document ID: 0942.62078
DOI: 10.1016/S0167-9473(99)00038-9
author: Ian McIntosh
author: Markus Hegland
author: Turlach, Berwin A.
published in: Computational Statistics and Data Analysis
publication date: 2000-06-04T00:00:00Z
review text: An implementation of the backfitting algorithm for generalised additive models which is suitable for parallel computing is described. This implementation is designed to handle large data sets such as those occurring in data mining with several millions of observations on several hundreds of variables. For such large data sets it is crucial to have a fast, parallel implementation for fitting generalised additive models to allow an exploratory analysis of the data within a reasonable time. The approach used divides the data into several blocks (groups) and fits a (generalised) additive model to each block. These models are then merged to a single, final model. It is shown that this approach is very efficient as it allows the algorithm to adapt to the structure of the parallel computer (number of processors and amount of internal memory).
Mathematics Subject Classification ID: 62J99
Mathematics Subject Classification ID: 62-07
Mathematics Subject Classification ID: 65C60
Mathematics Subject Classification ID: 62J12
Mathematics Subject Classification ID: 65Y05
zbMATH DE Number: 1454230
zbMATH Keywords: additive models
zbMATH Keywords: backfitting
zbMATH Keywords: data mining
zbMATH Keywords: local scoring
zbMATH Keywords: parallel algorithms
describes a project that uses: AS 176
describes a project that uses: KernSmooth
describes a project that uses: sm
MaRDI profile type: Publication
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full work available at URL: s0167-9473(99)00038-9
OpenAlex ID: W2091437388
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description: scientific article; zbMATH DE number 1454230