A parallel solver for generalised additive models

DOI10.1016/S0167-9473(99)00038-9zbMath0942.62078OpenAlexW2091437388MaRDI QIDQ1978411

Ian McIntosh, Markus Hegland, Turlach, Berwin A.

Publication date: 4 June 2000

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-9473(99)00038-9

zbMATH Keywords

additive modelsparallel algorithmsdata miningbackfittinglocal scoring

Mathematics Subject Classification ID

Generalized linear models (logistic models) (62J12) Linear inference, regression (62J99) Parallel numerical computation (65Y05)

Related Items

A distributed algorithm for fitting generalized additive models, Bounded optimal knots for regression splines, Parallel statistical computing for statistical inference

Uses Software

• KernSmooth
• sm
• AS 176

Cites Work

• Linear smoothers and additive models
• Smoothing by weighted averaging of rounded points
• The dimensionality reduction principle for generalized additive models
• Additive regression and other nonparametric models
• Nonparametric regression analysis of longitudinal data
• Accuracy of binned kernel functional approximations
• Fitting a bivariate additive model by local polynomial regression
• Methodology for nonparametric regression from independent sources
• Generalized additive models. With discussion
• On combining independent nonparametric regression estimators
• On the accuracy of binned kernel density estimators
• Smoothing methods in statistics
• Estimation of generalized additive models
• Finite-Sample Variance of Local Polynomials: Analysis and Solutions
• Estimating Optimal Transformations for Multiple Regression and Correlation
• Estimation of additive regression models with known links
• Algorithm AS 176: Kernel Density Estimation Using the Fast Fourier Transform
• On the backfitting algorithm for additive regression models
• A Fully Automated Bandwidth Selection Method for Fitting Additive Models
• Miscellanea. Efficient estimation of additive nonparametric regression models
• Interpolation Methods for Adapting to Sparse Design in Nonparametric Regression
• A kernel method of estimating structured nonparametric regression based on marginal integration
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