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"Plant Variety Selection Using Interaction Classes Derived From Factor " by Alison Smith, Adam Norman et al.

A major challenge in the analysis of plant breeding multi-environment datasets is the provision of meaningful and concise information for variety selection in the presence of variety by environment interaction (VEI). This is addressed in the current paper by fitting a factor analytic linear mixed model (FALMM) then using the fundamental factor analytic parameters to define groups of environments in the dataset within which there is minimal crossover VEI, but between which there may be substantial crossover VEI. These groups are consequently called interaction classes (iClasses). Given that the environments within an iClass exhibit minimal crossover VEI, it is then valid to obtain predictions of overall variety performance (across environments) for each iClass. These predictions can then be used not only to select the best varieties within each iClass but also to match varieties in terms of their patterns of VEI across iClasses. The latter is aided with the use of a new graphical tool c ....

Class Interaction , Crop Variety Evaluation , Actor Analytic Linear Mixed Models , Linear Mixed Models , Multi Environment Trials , Plant Breeding , Ariety By Environment Interaction ,

"Use of Contemporary Groups in the Construction of Multi-Environment Tr" by Alison Smith, Aanandini Ganesalingam et al.

Abstract
Plant breeding programs use multi-environment trial (MET) data to select superior lines, with the ultimate aim of increasing genetic gain. Selection accuracy can be improved with the use of advanced statistical analysis methods that employ informative models for genotype by environment interaction, include information on genetic relatedness and appropriately accommodate within-trial error variation. The gains will only be achieved, however, if the methods are applied to suitable MET datasets. In this paper we present an approach for constructing MET datasets that optimizes the information available for selection decisions. This is based on two new concepts that characterize the structure of a breeding program. The first is that of “contemporary groups,” which are defined to be groups of lines that enter the initial testing stage of the breeding program in the same year. The second is that of “data bands,” which are sequences of trials that correspond to the pr ....

Contemporary Groups , Linear Mixed Models , Model Based Design , Multi Environment Trials , Plant Breeding ,

"Small area estimation with linked data" by N. Salvati, E. Fabrizi et al.

Abstract
Data linkage can be used to combine values of the variable of interest from a national survey with values of auxiliary variables obtained from another source, such as a population register, for use in small area estimation. However, linkage errors can induce bias when fitting regression models; moreover, they can create non-representative outliers in the linked data in addition to the presence of potential representative outliers. In this paper, we adopt a secondary analyst’s point of view, assuming that limited information is available on the linkage process, and develop small area estimators based on linear mixed models and M-quantile models to accommodate linked data containing a mix of both types of outliers. We illustrate the properties of these small area estimators, as well as estimators of their mean squared error, by means of model-based and design-based simulation experiments. We further illustrate the proposed methodology by applying it to linked data fro ....

European Survey On Income , European Survey , Living Conditions , Exchangeable Linkage Error , Inite Population Inference , Linear Mixed Models , Ean Squared Error Estimation , Robust Estimation , ஐரோப்பிய கணக்கெடுப்பு , வாழும் நிபந்தனைகள் ,