  Prediction is critical for decision-making under uncertainty and lends
validity to statistical inference. With targeted prediction, the goal is to
optimize predictions for specific decision tasks of interest, which we
represent via functionals. Although classical decision analysis extracts
predictions from a Bayesian model, these predictions are often difficult to
interpret and slow to compute. Instead, we design a class of parametrized
actions for Bayesian decision analysis that produce optimal, scalable, and
simple targeted predictions. For a wide variety of action parametrizations 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.
