Statistics Seminar: Emi Tanaka (Uinversity of Sydney) -- Outlier detection for a complex linear mixed model: an application to plant breeding trials

Abstract: 

Outlier detection is an important preliminary step in the data analysis often 
conducted through a form of residual analysis. A complex data, such as those 
that are analysed by linear mixed models, gives rise to distinct levels of 
residuals and thus offers additional challenges for the development of an 
outlier detection method. Plant breeding trials are routinely conducted over 
years and multiple locations with the aim to select the best genotype as 
parents or commercial release. These so-called multi-environmental trials 
(MET) is commonly analysed using linear mixed models which may include cubic 
splines and autoregressive process to account for spatial trends. We consider 
some statistics derived from mean and variance shift outlier model (MSOM/VSOM) 
and the generalised Cook’s distance (GCD) for outlier detection. We present a 
simulation study based on a set of real wheat yield trials.