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Activity Number: 313
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #319353 View Presentation
Title: Imputing Data That Are Missing at High Rates Using a Boosting Algorithm
Author(s): Katherine Cauthen* and Gregory Lambert and Jaideep Ray and Sophia Lefantzi
Companies: Sandia National Laboratories and Sandia National Laboratories and Sandia National Laboratories and Sandia National Laboratories
Keywords: multiple imputation ; machine-learning ; boosting

Traditional multiple imputation approaches may perform poorly for datasets with high rates of missingness unless many m imputations are used. This paper implements an alternative machine learning-based approach to imputing data that are missing at high rates. We use boosting to create a strong learner from a weak learner fitted to a dataset missing many observations. This approach may be applied to a variety of types of learners (models). The approach is demonstrated by application to a spatiotemporal dataset for predicting dengue outbreaks in India from meteorological covariates. A Bayesian spatiotemporal CAR model is boosted to produce imputations, and the overall RMSE from a k-fold cross-validation is used to assess imputation accuracy.

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

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