Mixed models are one of the most widely used tools in applied statistics. Applications range from biostatistics (clinical trials, longitudinal studies), agricultural/biological (animal and plant breeding), environmental/epidemiological (spatial nutrient, pollutant and disease mapping) to educational and social sciences (multi-level modelling).

Basic mixed models which had their origins in the analysis of designed experiments have been expanded to allow for non-Gaussian random effects, larger data-sets or complex variance modelling. The explosion of applications in the bioinformatics area where mixed models are being applied to the analysis of QTL experiments or the analysis of microarray data require both complex variance modelling and computational efficiency to handle the larger numbers of random effects. In this track we present an overview of the origins of mixed models, through to more recent innovations with particular reference to the analysis of genomics data and non-parametric regression.

Focus:

Linear mixed models
Generalised linear mixed models
Smoothing and non-parametric regression
Imputation
Genomics

Co-Chairs:

Roger Payne Rothamsted Experimental Station AL5 2JQ Harpenden UK Tel: +44 582 763133 x2389 Fax: +44 582 467116 E-MAIL: roger.payne@bbsrc.ac.uk	Dr Brian R. Cullis Department of Primary Industries Pine Gully Road WAGGA WAGGA NSW 2650 Australia Tel: +61 2 69 381 883 Fax: +61 2 69 381 995 E-mail: brian.cullis@agric.nsw.gov.au