Fast, Optimal, and Targeted Predictions using Parametrized Decision Analysis.
Fast, Optimal, and Targeted Predictions using Parametrized Decision Analysis.
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DOI：
10.1080/01621459.2021.1891926
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发表时间：
2022
影响因子：
3.7
通讯作者：
Kowal, Daniel R.
中科院分区：
文献类型：
--
作者：
Kowal, Daniel R.
预测对于不确定情况下的决策至关重要，并为统计推断提供了有效性。有针对性的预测，目标是为我们通过函数表示的感兴趣的特定决策任务优化预测。尽管经典决策分析从贝叶斯模型中提取预测，但这些预测通常难以解释且计算缓慢。相反，我们为贝叶斯决策分析设计了一类参数化操作，这些操作可以产生最优的、可扩展的、简单的目标预测。对于各种各样的动作参数化和损失函数，包括目标变量选择的具有稀疏性约束的线性动作，我们推导出最优目标预测的方便表示，产生有效和可解释的解决方案。开发定制的样本外预测指标来评估和比较目标预测因子。通过仔细使用后验预测分布，我们引入了一种识别一组接近最优或可接受的目标预测器的程序，这些预测器提供了对精确目标预测所需的特征和复杂性水平的独特见解。模拟演示了出色的预测、估计和变量选择能力。针对全国健康与营养调查（NHANES）的身体活动数据构建针对性预测，以更好地预测和了解日内身体活动的特征。
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.
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DOI：
10.1198/106186008x320456
发表时间：
2008-06-01
影响因子：
2.4
作者：
Ionides, Edward L.
通讯作者：
Ionides, Edward L.
影响因子：
4.1
作者：
Fishman EI;Steeves JA;Zipunnikov V;Koster A;Berrigan D;Harris TA;Murphy R
通讯作者：
Murphy R
影响因子：
2.7
作者：
Goutis, C;Robert, CP
通讯作者：
Robert, CP
影响因子：
5.7
作者：
Rao, J. N. K.
通讯作者：
Rao, J. N. K.
DOI：
10.1080/01621459.2019.1672556
发表时间：
2019-10-30
影响因子：
3.7
作者：
Lei, Jing
通讯作者：
Lei, Jing