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Title: Outlier detection in a complex linear mixed model: An application in plant breeding trial Authors:  Emi Tanaka - University of Sydney (Australia) [presenting]
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 present the use of a mean/variance shift outlier model that fits well into the standard framework of linear mixed models, thus can be easily incorporated into a standard linear mixed model software, and is computationally efficient for routine use. We illustrate its application to a simulation based on a set of real wheat yield trials.