### Abstract

A linear mixed model version of penalised regression is proposed. We

demonstrate how to estimate the effects and standard errors of a set of intercorrelated explanatory variables by imposing penalties on the regression coefficients by modelling them as random effects. An example is given using correlated weather covariates.

demonstrate how to estimate the effects and standard errors of a set of intercorrelated explanatory variables by imposing penalties on the regression coefficients by modelling them as random effects. An example is given using correlated weather covariates.

Original language | English |
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Title of host publication | Statistical Solutions to Modern Problems. Proceedings of the 20th International Workshop on Statistical Modelling |

Editors | A R Francis, K M Matawie, A Oshlack, G K Smyth |

Place of Publication | Sydney, Australia |

Publisher | Statistical Modelling Society |

Pages | 447-451 |

ISBN (Print) | 1 74108 101 7 |

Publication status | Published - 2005 |

Event | 20th International Workshop on Statistical Modelling - Sydney, Australia Duration: 10 Jul 2005 → 15 Jul 2005 |

### Conference

Conference | 20th International Workshop on Statistical Modelling |
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Country | Australia |

City | Sydney |

Period | 10/07/05 → 15/07/05 |

## Fingerprint Dive into the research topics of 'Using mixed models to smooth regression coefficients corresponding to inter-correlated explanatory variables: an application to weather covariates'. Together they form a unique fingerprint.

## Cite this

Sims, M., Elston, D. A., & Larkham, A. (2005). Using mixed models to smooth regression coefficients corresponding to inter-correlated explanatory variables: an application to weather covariates. In A. R. Francis, K. M. Matawie, A. Oshlack, & G. K. Smyth (Eds.),

*Statistical Solutions to Modern Problems. Proceedings of the 20th International Workshop on Statistical Modelling*(pp. 447-451). Statistical Modelling Society.