A Diagnostic Procedure for Detecting Outliers in Linear State–Space Models
A Diagnostic Procedure for Detecting Outliers in Linear State–Space Models
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DOI：
10.1080/00273171.2019.1627659
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发表时间：
2020-03
影响因子：
3.8
通讯作者：
Dongjun You;Michael D. Hunter;Meng Chen;Sy-Miin Chow
中科院分区：
文献类型：
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作者：
Dongjun You;Michael D. Hunter;Meng Chen;Sy-Miin Chow
由于纵向数据的相关性，离群值在纵向数据中比在独立观测中更成问题。通常的做法是丢弃离群值，因为它们通常被视为数据中的干扰或畸变。然而，离群值也可以传达有关潜在模型错误指定的有意义的信息，以及修改和改进模型的方法。此外，在潜在变量中出现的异常值（创新异常值）与影响观察变量的异常值（加性异常值）相比具有不同的特征，并且最好使用不同的检验统计量和检测程序进行评估。我们证明和评估的性能的离群值检测方法的多学科的状态空间模型在蒙特卡洛模拟研究，相应的适应，以提高功率和降低误检率。此外，我们证明了所提出的方法的经验效用，使用的数据从生态瞬时评估研究的情绪调节连同一个开源软件实现的程序。
Abstract 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.