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