Project and Master Theses supervised by Stefanie Muff
My research interest lie at the interface between Bayesian statistics and evolutionary biology / movement ecology. Particular research areas include
- Quantitative genetics: This area offers a lot of statistical problems, mainly in the context of multivariate generalized linear mixed models. One main aim is to estimate variance components from genetic versus environmental components. Complex dependency structures between related individuals (derived from pedigree or genomic data), or environmental factors of wild study populations need to be properly accounted for. More recently, genomic data has opened new opportunities and statistical challenges, where master students can work on.
- Methods to analyse telemetry data: As it has become cheaper in the past years to equip wild animals with GPS collars to understand their resource preferences, there is a growing need in improved quantitative tools to analyse and interpret these data. There exists a wild variety of approaches to analyze such data, which are often assumed to be generated according to an inhomogeneous Poisson process. One aspect that we can look into is the problem of GPS error, which will have an effect of estimators and their accuracy, and ultimately on the conclusions drawn from the data.
In addition, I have been working on the problem of measurement error in variables of regression methods, namely for GLMMs and survival models. There are some interesting open questions regarding the effect and methods to account for such errors. Importantly, there are many mechanisms by which measurement error (often understood as measurement uncertainty) can emergy, but the most fundamental difference is that between classical and Berkson measurement error. I have coded two Shiny apps here: