Planned seminars

Europe/Lisbon

, The University of Edinburgh, The Roslin Institute

In breeding programmes, the observed genetic change is a sum of the contributions of different groups of individuals. Quantifying these sources of genetic change is essential for identifying the key breeding actions and optimizing breeding programmes. However, it is difficult to disentangle the contribution of individual groups due to the inherent complexity of breeding programmes. Here we extend the previously developed method for partitioning genetic mean by paths of selection to work both with the mean and variance of breeding values. We first extended the partitioning method to quantify the contribution of different groups to genetic variance assuming breeding values are known. Second, we combined the partitioning method with the Markov Chain Monte Carlo approach to draw samples from the posterior distribution of breeding values and use these samples for computing the point and interval estimates of partitions for the genetic mean and variance. We implemented the method in the R package AlphaPart. We demonstrated the method with a simulated cattle breeding programme.We showed how to quantify the contribution of different groups of individuals to genetic mean and variance. We showed that the contributions of different selection paths to genetic variance are not necessarily independent. Finally, we observed some limitations of the partitioning method under a misspecified model, suggesting the need for a genomic partitioning method. We presented a partitioning method to quantify sources of change in genetic mean and variance in breeding programmes. The method can help breeders and researchers understand the dynamics in genetic mean and variance in a breeding programme. The developed method for partitioning genetic mean and variance is a powerful method for understanding how different paths of selection interact within a breeding programme and how they can be optimised.

Joint seminar CEMAT and CEAUL

Europe/Lisbon

, Tilburg University, The Netherlands

We extend extreme value statistics to independent data with possibly very different distributions. In particular, we present novel asymptotic normality results for the Hill estimator, which now estimates the positive extreme value index of the average distribution. Due to the heterogeneity, the asymptotic variance can be substantially smaller than that in the i.i.d. case. As a special case, we consider a heterogeneous scales model where the asymptotic variance can be calculated explicitly. The primary tool for the proofs is the functional central limit theorem for a weighted tail empirical process. A simulation study shows the good finite-sample behavior of our limit theorems. We present an application to assess the tail heaviness of earthquake energies. This is joint work with Yi He (Univ. of Amsterdam).

Joint seminar CEMAT and CEAUL

Europe/Lisbon Unusual schedule

, Research Center for Statistics, University of Geneva
To be announced

Europe/Lisbon
Online

, King’s College London, UK
To be announced

Joint seminar CEMAT and CEAUL