biostatistics

The high-dimensional character of genomics data generally forces statistical inference methods to apply some form of penalization, e.g. multiple testing, penalized regression or sparse gene networks. The other side of the coin, however, is that the dimension of the variable space may also be used to learn across variables (like genes, tags, methylation probes, etc). Empirical Bayes is a powerful principle to do so. In both the Bayesian and frequentist paradigms it comes down to estimation of the a priori distribution of parameter(s) from the data.

By Vanessa Didele from University of Bristol, U.K.
Causal Models for Optimal Dynamic Treatments with Survival Outcomes