Gal Novich, M.Sc. Thesis Seminar
Thursday, 5.9.2019, 10:00
Advisor: Prof. Roy Kishony, Prof. Zohar Yakhini
In the field of population genetics, it is known that collected observations are not independent, as they all share a common ancestor. Current state-of-the-art association tests use Monte Carlo simulations to account for these dependency structures. However, these simulations are not scalable and are cumbersome to use for the evaluation of only a few parameters.
In our work, we introduce a generalized, simulation-free analytical test that accounts for hierarchical sample dependency structures. We formulate our model assumptions and compare our performance to the existing state of the art.
Our method is widely applicable, as hierarchical sample dependency structures exists in many types of real-world data. To showcase the strength and generality of our method, we present an analysis of several case studies of social media data from YouTube and Wikipedia