A note on P-spline additive models with correlated errors

DOI10.1007/s001800300143zbMath1050.62042OpenAlexW10028449MaRDI QIDQ1887219

Iain D. Currie, María Durbán

Publication date: 24 November 2004

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s001800300143

zbMATH Keywords

estimationmodel selectionserial correlationmixed modeladditive modelpenalized splineback-fittingautoregression model

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Numerical computation using splines (65D07) Numerical smoothing, curve fitting (65D10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Generalized linear models (logistic models) (62J12)

Related Items (9)

General design Bayesian generalized linear mixed models ⋮ On estimation of nonparametric regression models with autoregressive and moving average errors ⋮ Exploring US business cycles with bivariate loops using penalized spline regression ⋮ Semiparametric regression during 2003--2007 ⋮ Direct Simultaneous Inference in Additive Models and Its Application to Model Undernutrition ⋮ Smoothing and forecasting mortality rates ⋮ P-spline ANOVA-type interaction models for spatio-temporal smoothing ⋮ A general framework for prediction in penalized regression ⋮ Parsimonious classification via generalized linear mixed models

Uses Software

• S-PLUS
• MEMSS

Cites Work

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• Some theory for penalized spline generalized additive models
• Simple Incorporation of Interactions into Additive Models
• Flexible smoothing with P-splines: a unified approach
• Generalized Additive Models: Some Applications
• Smoothing Spline Models with Correlated Random Errors
• Additive Nonparametric Regression With Autocorrelated Errors
• The Analysis of Designed Experiments and Longitudinal Data by Using Smoothing Splines
• Mixed-Effects Models in S and S-PLUS
• Approximate Standard Errors in Semiparametric Models
• Respiratory health and air pollution: additive mixed model analyses

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