Computing confidence intervals from massive data via penalized quantile smoothing splines

Abstract

New methodology is presented for the computation of pointwise confidence intervals from massive response data sets in one or two covariates using robust and flexible quantile regression splines. Novel aspects of the method include a new cross-validation procedure for selecting the penalization coefficient and a reformulation of the quantile smoothing problem based on a weighted data representation. These innovations permit for uncertainty quantification and fast parameter selection in very large data sets via a distributed “bag of little bootstraps”. Experiments with synthetic data demonstrate that the computed confidence intervals feature empirical coverage rates that are generally within 2% of the nominal rates. The approach is broadly applicable to the analysis of large data sets in one or two dimensions. Comparative (or “A/B”) experiments conducted at Netflix aimed at optimizing the quality of streaming video originally motivated this work, but the proposed methods have general applicability. The methodology is illustrated using an open source application: the comparison of geo-spatial climate model scenarios from NASA’s Earth Exchange.