Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data.
Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data.
复制标题
多学科密集纵向数据的惩罚估计和预测。
DOI：
10.1007/s11336-021-09825-7
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发表时间：
2022
期刊：
影响因子：
3
通讯作者：
Pipiras,Vladas
中科院分区：
文献类型：
--
作者：
Fisher,ZacharyF;Kim,Younghoon;Fredrickson,BarbaraL;Pipiras,Vladas
密集纵向数据（ILD）是社会和行为科学中越来越常见的数据类型。尽管这些数据提供了许多好处，但很少有人致力于实现这些数据在个人层面预测动态过程的潜力。为了解决文献中的这一差距，我们提出了多VAR框架，一种新的方法学方法，允许从多个个体收集ILD的惩罚估计。重要的是，我们的方法同时估计所有个体的模型，并且能够适应性地调整个体动态过程中存在的异质性。为了实现这一点，我们提出了一种新的近似梯度下降算法来解决多VAR问题，并证明了恢复的过渡矩阵的一致性。我们评估我们的方法与一些基准方法相比的预测性能，并提供了一个说明性的例子，涉及16个人在11周内的日常情绪体验。
Intensive longitudinal data (ILD) is an increasingly common data type in the social and behavioral sciences. Despite the many benefits these data provide, little work has been dedicated to realize the potential such data hold for forecasting dynamic processes at the individual level. To address this gap in the literature, we present the multi-VAR framework, a novel methodological approach allowing for penalized estimation of ILD collected from multiple individuals. Importantly, our approach estimates models for all individuals simultaneously and is capable of adaptively adjusting to the amount of heterogeneity present across individual dynamic processes. To accomplish this, we propose a novel proximal gradient descent algorithm for solving the multi-VAR problem and prove the consistency of the recovered transition matrices. We evaluate the forecasting performance of our method in comparison with a number of benchmark methods and provide an illustrative example involving the day-to-day emotional experiences of 16 individuals over an 11-week period.