Biswas Atanu, Guha Apratim
Applied Statistics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata, 700 108, India.
School of Mathematics, University of Birmingham, Watson Building, Birmingham, B15 2TT, UK.
J Comput Neurosci. 2010 Aug;29(1-2):35-47. doi: 10.1007/s10827-009-0165-3. Epub 2009 May 28.
Multivariate time series data of which some components are continuous time series and the rest are point processes are called hybrid data. Such data sets routinely arise while working with neuroscience data, EEG and spike trains would perhaps be the most obvious example. In this paper, we discuss the modeling of a hybrid time series, with the continuous component being the physiological tremors in the distal phalanx of the middle finger, and motor unit firings in the middle finger portion of the extensor digitorum communis (EDC) muscle. We employ a model for the two components based on Auto-regressive Moving Average (ARMA) type models. Another major issue to arise in the modeling of such data is to assess the goodness of fit. We suggest a visual procedure based on mutual information towards assessing the dependence pattern of hybrid data. The goodness of fit is also verified by standard model fitting diagnostic techniques for univariate data.
一些分量是连续时间序列而其余分量是点过程的多元时间序列数据被称为混合数据。在处理神经科学数据时经常会出现这样的数据集，脑电图（EEG）和尖峰序列可能是最明显的例子。在本文中，我们讨论混合时间序列的建模，其中连续分量是中指远端指骨的生理震颤，以及指总伸肌（EDC）肌肉中指部分的运动单位放电。我们基于自回归移动平均（ARMA）类型模型为这两个分量采用一种模型。对此类数据进行建模时出现的另一个主要问题是评估拟合优度。我们提出一种基于互信息的可视化方法来评估混合数据的依赖模式。拟合优度也通过单变量数据的标准模型拟合诊断技术来验证。
