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Activity Number: 468 - Statistical Challenges and Novel Methodologies for Analyzing Health Outcomes
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #323572
Title: Analysis of Large Data with Subsampling
Author(s): Zhezhen Jin* and Yujing Yao
Companies: Columbia University and Columbia University
Keywords: large data; subsampling; perturbation

Analysis of large data is challenging due to its size and computational issues. Subsampling methods and divide-and-conquer procedures are appealing because they ease computational burden. However, it is challenging to preserve the validity of the resulting estimation and inference. In this talk, we will discuss a perturbation subsampling approach based on independent and identically distributed stochastic weights for the analysis of large data. We justify the method based on optimizing convex objective functions by establishing asymptotic consistency and normality for the resulting estimators. Simulation studies and real data analysis will also be used to illustrate the finite sample performance of the method.

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

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