Using mixed models to smooth regression coefficients corresponding to inter-correlated explanatory variables: an application to weather covariates

Michelle Sims; D A Elston; A Larkham

Using mixed models to smooth regression coefficients corresponding to inter-correlated explanatory variables: an application to weather covariates

Michelle Sims, D A Elston, A Larkham

Department for Health

Research output: Chapter or section in a book/report/conference proceeding › Chapter or section

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.

Original language	English
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
Country/Territory	Australia
City	Sydney
Period	10/07/05 → 15/07/05

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.

Using mixed models to smooth regression coefficients corresponding to inter-correlated explanatory variables: an application to weather covariates. / Sims, Michelle; Elston, D A; Larkham, A.
Statistical Solutions to Modern Problems. Proceedings of the 20th International Workshop on Statistical Modelling. ed. / A R Francis; K M Matawie; A Oshlack; G K Smyth. Sydney, Australia: Statistical Modelling Society, 2005. p. 447-451.

Research output: Chapter or section in a book/report/conference proceeding › Chapter or section

Sims, M, Elston, DA & Larkham, A 2005, Using mixed models to smooth regression coefficients corresponding to inter-correlated explanatory variables: an application to weather covariates. in AR Francis, KM Matawie, A Oshlack & GK Smyth (eds), Statistical Solutions to Modern Problems. Proceedings of the 20th International Workshop on Statistical Modelling. Statistical Modelling Society, Sydney, Australia, pp. 447-451, 20th International Workshop on Statistical Modelling, Sydney, New South Wales, Australia, 10/07/05.

Sims M, Elston DA, Larkham A. Using mixed models to smooth regression coefficients corresponding to inter-correlated explanatory variables: an application to weather covariates. In Francis AR, Matawie KM, Oshlack A, Smyth GK, editors, Statistical Solutions to Modern Problems. Proceedings of the 20th International Workshop on Statistical Modelling. Sydney, Australia: Statistical Modelling Society. 2005. p. 447-451

Sims, Michelle ; Elston, D A ; Larkham, A. / Using mixed models to smooth regression coefficients corresponding to inter-correlated explanatory variables: an application to weather covariates. Statistical Solutions to Modern Problems. Proceedings of the 20th International Workshop on Statistical Modelling. editor / A R Francis ; K M Matawie ; A Oshlack ; G K Smyth. Sydney, Australia : Statistical Modelling Society, 2005. pp. 447-451

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Using mixed models to smooth regression coefficients corresponding to inter-correlated explanatory variables: an application to weather covariates

Abstract

Conference

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