Should We Model X in High-Dimensional Inference?

9/20/18 | 4:15pm | E51-335
Reception to follow.


 

 

 

 

Lucas Janson

Assistant Professor
Harvard University 


Abstract: For answering questions about the relationship between a response variable Y and a set of explanatory variables X, most statistical methods focus their assumptions on the conditional distribution of Y given X (or Y | X for short). I will describe some benefits of shifting those assumptions from the conditional distribution Y | X to the joint distribution of X, especially when X is high-dimensional. First, assuming a model for X can often more closely match available domain knowledge, and allows for model checking and robustness that is unavailable when modeling Y | X. Second, there are substantial methodological payoffs in terms of interpretability, flexibility of models, and adaptability of algorithms for quantifying a hypothesized effect, all while being guaranteed exact (non-asymptotic) inference. I will briefly mention some of my recent and ongoing work on methods for high-dimensional inference that model X instead of Y | X, as well as some challenges and interesting directions for the future in this area.

Bio: Lucas Janson is an Assistant Professor in the Department of Statistics at Harvard University, where he develops methodology for high-dimensional inference, robust machine learning, and autonomous robotic motion planning. He is interested in applying his work to real data, with examples so far including genetics, climate science, and health care. Prior to Harvard, he was a Ph.D. student in Stanford University's Statistics Department, where he was advised by Emmanuel Candès. In 2011, he received a B.S. in mathematics (honors thesis advised by Bala Rajaratnam) and physics, as well as a M.S. in statistics, from Stanford University.

Event Time: 

2018 - 16:15