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Friday, June 23 • 5:04pm - 5:16pm
MultipleTesting.jl: Simultaneous Statistical Inference in Julia

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The parallel application of multiple statistical hypothesis tests is one of the fundamental patterns of exploratory data analysis for big datasets. This becomes essential in various fields of scientific research, such as in high-throughput biology, medicine and imaging where one is routinely faced with millions of tests. The goal is to protect against spurious discoveries with rigorous statistical error control guarantees, while simultaneously providing enough power to detect needles in a haystack. Here, we present MultipleTesting.jl, a package that provides a unified interface for classical and modern multiple testing methods. We give a quick introduction to the underlying statistical concepts and show how Julia is ideally suited for such an endeavour: First, most multiple testing procedures consist of a standard set of primitives, such as p-values, adjusted p-values and hypothesis weights. Second, elaborate (multiple testing) algorithms often consist of simpler components in a plug-and-play fashion; these include estimators of the proportion of true null hypotheses, parametric as well as non-parametric distribution estimators, and statistical machine learning techniques. All of these ideas can be abstracted away by Julia's type system and multiple dispatch. Third, Julia provides the computational performance which is necessary when analyzing millions of hypotheses. We believe MultipleTesting.jl complements the growing number of high quality statistics packages in Julia's ecosystem.


Nikolaos Ignatiadis

Stanford University
Nikos Ignatiadis is a first year Ph.D. student at Stanford’s Statistics department. He is interested in the development of interpretable methods for multiple testing and high dimensional inference.

Friday June 23, 2017 5:04pm - 5:16pm PDT
West Pauley Pauley Ballroom, Berkeley, CA