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Activity Number: 509 - Recent Advances in High-Dimensional Time Series Analysis
Type: Topic Contributed
Date/Time: Thursday, August 11, 2022 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract #320994
Title: Fast, Optimal, and Targeted Predictions Using Parametrized Decision Analysis
Author(s): Daniel Kowal*
Companies: Rice University
Keywords: Bayesian statistics; functional data; physical activity; variable selection; time series
Abstract:

In targeted prediction, the goal is to optimize predictions for specific decision tasks of interest. Using Bayesian decision analysis, we design a class of parametrized actions that produce optimal, scalable, and simple targeted predictions. For a wide variety of actions and loss functions--including linear actions with sparsity constraints for targeted variable selection--we derive a convenient representation of the optimal targeted prediction that yields efficient and interpretable solutions. Customized out-of-sample predictive metrics are developed to evaluate and compare among targeted predictors. Through careful use of the posterior predictive distribution, we introduce a procedure that identifies a set of near-optimal, or acceptable targeted predictors, which provide unique insights into the features and level of complexity needed for accurate targeted prediction. Simulations demonstrate excellent prediction, estimation, and variable selection capabilities. Targeted predictions are constructed for physical activity data from the National Health and Nutrition Examination Survey (NHANES) to better predict and understand the characteristics of intraday physical activity.


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