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Activity Number: 10 - Challenges and Breakthroughs in Analyzing Big Survey Data
Type: Invited
Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
Sponsor: Government Statistics Section
Abstract #300377
Title: Relationship Mining in Big Data from Surveys Using Penalization and the Bag-Of-Little-Bootstraps
Author(s): Snigdhansu Chatterjee* and Benjamin E. Bagozzi and Ujjal Kumar Mukherjee and Xuetong Sun
Companies: University of Minnesota and University of Delaware and University of Illinois and University of Minnesota
Keywords: Data mining; survey; high dimensional; rare event; penalization; bag-of-little bootstraps

We discuss high dimensional and large scale inference procedures under various survey distortions, systematic and non-ignorable non-responses and drop-outs, using a bag-of-little-bootstraps and penalization-based approach. We present a scheme for inference with biased survey data. Our methods may be used for studying how probabilities of rare events may be linked to explanatory variables even in biased data. We present several theoretical results, and illustrate the proposed methodology on data on political conflicts gathered from multiple federal agencies.

Authors who are presenting talks have a * after their name.

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