Jun Liu : On the detection of non-independence

Posted by Ryan Martin , part of the Departmental Colloquium.

At

Nov. 14, 2014, 3 p.m.

In

SEO 636

Abstract

I will discuss a few recent results from my group aiming to the detection of non-linear dependence between two random variables. Our approach is based on an optimal slicing (discretization) of one or both variables to optimize a score function derived from a likelihood-ratio test formulation. Our approaches are compared with some well-known methods such as Distance Correlation, Pearson Correlation, Maximal Information Criterion, etc., on many simulated examples, and found superior for highly nonlinear and non-smooth relationships between the two variables. We will also show how these methods are applied to bioinformatics problems such as gene-set enrichment analysis, transcription regulation analysis, etc.