Announcing the winner of our second competition - Jackknife regression

The winner for our second data science competition is Tom De Smedt, biostatistician completing a Ph.D program at University of Leuven, Belgium. His special interests are in spatial statistics, environmental epidemiology, novel regression techniques and data visualization. The competition consisted of simulating data and testing the Jackknife regression technique recently developed in our laboratory, on correlated features or variables. The technique provides an approximation to standard regression, but is far more robust and deemed suitable for automated or black-box data science. The easiest version consists of pretending that variables are uncorrelated, to very quickly obtain robust regression coefficients that are easy to interpret.