Pennsylvania State University, State College, Pennsylvania, USA.
Georgia Institute of Technology, Atlanta, Georgia, USA.
Multivariate Behav Res. 2020 Mar-Apr;55(2):231-255. doi: 10.1080/00273171.2019.1627659. Epub 2019 Jul 2.
Outliers can be more problematic in longitudinal data than in independent observations due to the correlated nature of such data. It is common practice to discard outliers as they are typically regarded as a nuisance or an aberration in the data. However, outliers can also convey meaningful information concerning potential model misspecification, and ways to modify and improve the model. Moreover, outliers that occur among the latent variables (innovative outliers) have distinct characteristics compared to those impacting the observed variables (additive outliers), and are best evaluated with different test statistics and detection procedures. We demonstrate and evaluate the performance of an outlier detection approach for multi-subject state-space models in a Monte Carlo simulation study, with corresponding adaptations to improve power and reduce false detection rates. Furthermore, we demonstrate the empirical utility of the proposed approach using data from an ecological momentary assessment study of emotion regulation together with an open-source software implementation of the procedures.
由于此类数据的相关性，异常值在纵向数据中比在独立观测中更成问题。通常的做法是丢弃异常值，因为它们通常被视为数据中的干扰或异常。然而，异常值也可以传达有关潜在模型失拟的有意义信息，以及修改和改进模型的方法。此外，在潜在变量（创新异常值）中出现的异常值与影响观测变量（附加异常值）的异常值具有不同的特征，并且最好使用不同的检验统计量和检测程序进行评估。我们在蒙特卡罗模拟研究中演示和评估了一种用于多主体状态空间模型的异常值检测方法的性能，并进行了相应的调整，以提高功效并降低错误检测率。此外，我们使用情绪调节的生态瞬时评估研究的数据以及这些程序的开源软件实现来演示所提出方法的实际效用。
