SMH Statistics Reading Group

Next session: Wednesday 27th March, with an exciting paper on a topic new to us!

Paper: “Multilevel Monte Carlo methods”, M. B. Giles, Acta Numerica, 2015, vol. 24, pp. 259-328

https://www.cambridge.org/core/journals/acta-numerica/article/multilevel-monte-carlo-methods/C5AF9A57ED8FF8FDF08074C1071C5511

Monte Carlo (MC) methods are a very general and useful approach for the estimation of expectations arising from stochastic simulation. However, they can be computationally **very** expensive. Multilevel Monte Carlo is a recently developed approach which greatly reduces the computational cost by performing most simulations with low accuracy at a correspondingly low cost, with relatively few simulations being performed at high accuracy and a high cost. This article reviews the ideas behind multilevel MC, and various cool applications.

Very relevant for infectious disease modeling!

Presenter: Juliette Unwin

Read Next session – 27/03 – Multilevel Monte-Carlo in full

please join us this coming Wednesday 6th March for the next round of the Stats reading group!

Where: MSc student room

When: 11am

Presenter: Ville Karhunen

We will be looking at the following paper

“Polygenic scores via penalized regression on summary statistics”, Mak et al, 2017. Genetic Epidemiology,  vol. 41 issue 6

Polygenic scores (PGS) summarize the genetic contribution of a person’s genotype to a disease or phenotype. They can be used to group participants into different risk categories for diseases, and are also used as covariates in epidemiological analyses. Recently, there is much interest in methods that use published summary statistics. However there is no inherent information on linkage disequilibrium (LD) in summary statistics, so we have to use LD information available elsewhere. The authors propose a method for constructing PGS using summary statistics and a reference panel in a penalized regression framework.

https://doi.org/10.1002/gepi.22050

Read Wed. 04/03 – PGS and LASSO paper in full