Planned seminars

Europe/Lisbon
Room P3.10, Mathematics Building Instituto Superior Técnicohttps://tecnico.ulisboa.pt

, European University Viadrina, Department of Statistics, Frankfurt, Germany

In this presentation new types of multivariate EWMA control charts are presented. They are based on the Euclidean distance and on the distance defined by using the inverse of the diagonal matrix consisting of the variances. The design of the proposed control schemes does not involve the computation of the inverse covariance matrix and, thus, it can be used in the high-dimensional setting. The distributional properties of the control statistics are obtained and are used in the determination of the new control procedures. Within an extensive simulation study the new approaches are compared with the multivariate EWMA control charts which are based on the Mahalanobis distance.

The presented results are based on a joint work with Rostyslav Bodnar and Taras Bodnar.

Joint seminar CEMAT and CEAUL

Europe/Lisbon

Oswaldo Gressani, Hasselt University

Statistical methods play an important role in infectious disease epidemiology. They provide the main set of tools to compute estimates of key epidemiological parameters and to shed light on the transmission dynamics of a pathogen. Markov chain Monte Carlo (MCMC) methods are powerful simulation techniques used to explore the posterior parameter space and carry out inference under the Bayesian paradigm. As MCMC samplers are iterative by design, drawing samples from the target posterior distribution often requires huge computational resources. This computational bottleneck is particularly unwelcome when analysis of epidemic data and estimation of model parameters is required in (near) real-time, as is often the case during epidemic outbreaks where massive datasets are updated on a daily basis. We explore the synergy between the Laplace approximation and Bayesian P-splines in epidemic models to deliver a flexible inference methodology with fast and nimble algorithms that outperform MCMC-based approaches from a computational perspective. The socalled “Laplacian-P-splines” method is illustrated in the context of nowcasting (i.e. the real-time assessment of the current epidemic situation corrected for imperfect data information caused by delays in reporting) and in the recently proposed EpiLPS framework for estimating the time-varying reproduction number with applications on data of SARS-CoV-2.

Joint seminar CEMAT and CEAUL

Europe/Lisbon

Thiago de Paula Oliveira, 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