We propose a new strictly stationary time series model with marginal generalized Gaussian distribution and exponentially decaying autocorrelation function for modeling of increments of electroencephalogram (EEG) data collected from Ugandan children during coma from cerebral malaria. The model inherits its appealing properties from the strictly stationary strong mixing Markovian diffusion with invariant generalized Gaussian distribution (GGD). The GGD parametrization used in this paper comprises some famous light-tailed distributions (e.g., Laplace and Gaussian) and some well known and widely applied heavy-tailed distributions (e.g., Student). Two versions of this model fit to the data from each EEG channel. In the first model, marginal distributions is from the light-tailed GGD sub-family, and the distribution parameters were estimated using quasilikelihood approach. In the second model, marginal distributions is heavy-tailed (Student), and the tail index was estimated using the approach based on the empirical scaling function. The estimated parameters from models across EEG channels were explored as potential predictors of neurocognitive outcomes of these children 6 months after recovering from illness. Several of these parameters were shown to be important predictors even after controlling for neurocognitive scores immediately following cerebral malaria illness and traditional blood and cerebrospinal fluid biomarkers collected during hospitalization.

中文翻译：

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脑电数据增量的广义高斯时间序列模型

我们提出了一种新的严格平稳的时间序列模型，该模型具有边缘广义高斯分布和指数衰减自相关函数，用于模拟从乌干达儿童脑疟疾昏迷期间收集的脑电图 (EEG) 数据的增量。该模型从具有不变广义高斯分布 (GGD) 的严格平稳的强混合马尔可夫扩散中继承了其吸引人的特性。本文中使用的 GGD 参数化包括一些著名的轻尾分布（例如，拉普拉斯和高斯分布）和一些众所周知且广泛应用的重尾分布（例如，学生）。该模型的两个版本适合来自每个 EEG 通道的数据。在第一个模型中，边际分布来自轻尾 GGD 亚家族，并使用拟似然方法估计分布参数。在第二个模型中，边际分布是重尾分布（Student），尾指数是使用基于经验尺度函数的方法估计的。来自 EEG 通道模型的估计参数被探索为这些儿童在疾病康复 6 个月后神经认知结果的潜在预测因子。即使在控制了脑疟疾疾病后立即的神经认知评分以及住院期间收集的传统血液和脑脊液生物标志物之后，其中一些参数也被证明是重要的预测因子。来自 EEG 通道模型的估计参数被探索为这些儿童在疾病康复 6 个月后神经认知结果的潜在预测因子。即使在控制了脑疟疾疾病后立即的神经认知评分以及住院期间收集的传统血液和脑脊液生物标志物之后，其中一些参数也被证明是重要的预测因子。来自 EEG 通道模型的估计参数被探索为这些儿童在疾病康复 6 个月后神经认知结果的潜在预测因子。即使在控制了脑疟疾疾病后立即的神经认知评分以及住院期间收集的传统血液和脑脊液生物标志物之后，其中一些参数也被证明是重要的预测因子。