Title: "Predictor Variable Prioritization in Nonlinear Models: A
Genetic Association Case Study" Abstract:
We address variable selection questions in nonlinear and nonparametric
regression. Motivated by statistical genetics, where nonlinear
interactions are of particular interest, we introduce a novel,
interpretable, and computationally efficient way to summarize the
relative importance of predictor variables. Methodologically, we
develop the “RelATive cEntrality” (RATE) measure to prioritize
candidate genetic variants that are not just marginally important, but
whose associations also stem from significant covarying relationships
with other variants in the data. We illustrate RATE through Bayesian
Gaussian process regression, but the methodological innovations apply
to other nonlinear methods. It is known that nonlinear models often
exhibit greater predictive accuracy than linear models, particularly
for phenotypes generated by complex genetic architectures. With
detailed simulations and an Arabidopsis thaliana QTL mapping study, we
show that applying RATE enables an explanation for this improved
performance. Bio:
Seth Flaxman is a lecturer in the statistics section of the
Department of Mathematics at Imperial College London, joint with the
Data Science Institute. His research is on scalable methods and
flexible models for spatiotemporal statistics and Bayesian machine
learning, applied to public policy and social science. He has worked
on application areas that include public health, crime, voting
patterns, filter bubbles / echo chambers in media, the regulation of
machine learning algorithms, and emotion. |