A low variance consistent test of relative dependency

We describe a novel non-parametric statistical hypothesis test of relative dependence between a source variable and two candidate target variables. Such a test enables us to determine whether one source variable is significantly more dependent on a first target variable or a second. Dependence is measured via the Hilbert-Schmidt Independence Criterion (HSIC), resulting in a pair of empirical dependence measures (source-target 1, source-target 2). We test whether the first dependence measure is significantly larger than the second. Modeling the covariance between these HSIC statistics leads to a provably more powerful test than the construction of independent HSIC statistics by sub-sampling. The resulting test is consistent and unbiased, and (being based on U-statistics) has favorable convergence properties. The test can be computed in quadratic time, matching the computational complexity of standard empirical HSIC estimators. The effectiveness of the test is demonstrated on several real-world problems: we identify language groups from a multilingual corpus, and we prove that tumor location is more dependent on gene expression than chromosomal imbalances. Source code is available for download at https://github.com/wbounliphone/reldep.Comment: International Conference on Machine Learning, Jul 2015, Lille, Franc

Stormwater Discussion Session - Recommendations

The Stormwater panel addressed water quality impacts on the Wakulla springshed stemming from stormwater discharge into the basin; correlation of discharge with precipitation; relationship between land use and the quantity and quality of runoff and use of Florida's stormwater program and BMP's to address these impacts; nutrient loading; local efforts to reduce stormwater pollution; and, the role of phosphorus vs. nitrate concentrations as triggers to eutrophication and Hydrilla and algal growth. The panel evaluated current stormwater runoff management practices and discussed field-tested and new and innovative practices that offer more environmentally sound and/or cost effective alternatives to stormwater management in karst. 1 slid