permute: A Python Package for Randomization Inference

2016 Annual Conference of the International Society for Non-Parametric Statistics (ISNPS)

Lecture

June 16, 2016
9:20am to 9:40am
Avignon, France

3rd Conference of the International Society for Non-Parametric Statistics (ISNPS)
Event Program

The International Society for NonParametric Statistics (ISNPS) was founded by Michael Akritas, Soumendra Lahiri and Dimitris Politis in 2010 with the mission "to foster the research and practice of nonparametric statistics, and to promote the dissemination of new developments in the field via conferences, books and journal publications."

The nature of ISNPS is uniquely global, and its international conferences are designed to facilitate the exchange of ideas and latest advances among researchers from all around the world in cooperation with established statistical societies such as the Institute of Mathematical Statistics (IMS) and the International Statistical Institute (ISI).

Speaker(s)

Kellie Ottoboni

Alumni - BIDS Data Science Fellow

Kellie Ottoboni is a former BIDS Data Science Fellow and a graduate of UC Berkeley's Department of Statistics. Her research at BIDS focused on using robust nonparametric statistics and machine learning to make causal inferences from data in the health and social sciences. The goal was to make reliable inferences while making minimal assumptions about the models generating the data. In addition to developing new statistical methods and studying their theoretical properties, Kellie wrote open source software implementing nonparametric methods in R and Python.