Comparing classical criteria for selecting intra-class correlated features in Multimix

The mixture approach to clustering requires the user to specify both the number of components to be fitted to the model and the form of the component distributions. In the Multimix class of models, the user also has to decide on the correlation structure to be introduced into the model. The behaviour of some commonly used model selection criteria is investigated when using the finite mixture model to cluster data containing mixed categorical and continuous attributes. The performance of these criteria in selecting both the number of components in the model and the form of the correlation structure amongst the attributes when fitting the Multimix class of models is illustrated using simulated data and a real medical data set. It is found that criteria based on the integrated classification likelihood have the best performance in detecting the number of clusters to be fitted to the model and in selecting the form of the component distributions. The performance of the Bayesian information criterion in detecting the correct model depends on the partitioning structure among the attributes while the Akaike information criterion and classification likelihood criterion perform in a less satisfactory way

IBS: Transforming our governance

After more than 60 years, the Legislative Council overwhelming approved a revised governance structure for the International Biometric Society (IBS) to take effect from 1 January 2012. Responsibility for the governance and leadership of the society will be combined and placed in the hands of an Executive Board, supported by a much larger Representative Council. The Representative Council will be composed of members selected by the different regions (or geographical components) of the society. It will be responsible for overseeing the nomination and election (by the whole society) of the Executive Board and provide the conduit between the regions and this leadership team. Members of the Representative Council will also chair the Standing Committees. The transition process to the new governance structure is outlined, as are focus issues for the next decade

Developing Optimal Selection Systems in Sugarcane Breeding Programs

SELECTION REPRESENTS a costly and important part of sugarcane breeding programs. Previous research showed that cane yields in small single-row plots are affected strongly by competition effects, and that a high weighting in selection indices should be placed on CCS in small single-row plots to maximise gains for economic value. This led to a new selection system being suggested, involving initial screening of large numbers of clones in 5 m plots with heavy selection pressure for CCS followed by two stages of selection in multi-row plots. A stochastic simulation model using assumptions on relevant parameters (genetic, error, competition, and GE variances, genetic correlations) was developed to predict gains from alternative selection systems. Field trials were conducted in the Burdekin region to assess realised gains from alternative selection trial designs to validate and refine assumptions important in the model. The model was then used to predict genetic gains in selection systems with a wide range of configurations (e.g. plot size, replicate number, number of sites, selection criteria, selection intensity in each stage, and number of stages). Based on the results, it was recommended that three stages of clonal selection (following current family selection in stage 1 be performed in core breeding programs. This should involve firstly screening clones in small (1 row × 5 m) plots, with a selection index biased strongly toward CCS, but also including cane yield estimated via visual grade. Selected clones should then be evaluated in two further stages – the first one consisting of 4-row plots at four sites with a single replicate per clone per site. Clones selected from this stage should then be evaluated in 4-row plots at four sites but with two replicates per site. The recommended system has been introduced in the Burdekin selection system for further practical evaluation and it is recommended that it be assessed in other regions. The research conducted here also emphasised the importance of using optimal selection indices in single-row plots, in order to maximise gains from selection