DSPA Chapter 18 Big Longitudinal Data Analysis (Timeseries GEE GLMM SEM)The time-varying (longitudinal) characteristics of large information
flows represent a special case of the complexity, dynamic and
multi-scale nature of big biomedical data that we discussed in the DSPA Motivation setion. Previously, in Chapter 3, we saw space-time (4D) functional magnetic resonance imaging (fMRI) data, and in Chapter 5 we discussed streaming data, which also has a natural temporal dimension.
In this Chapter, we will expand our predictive data analytic…Read more
DSPA Chapter 18 Big Longitudinal Data Analysis (Timeseries GEE GLMM SEM)

The time-varying (longitudinal) characteristics of large information
flows represent a special case of the complexity, dynamic and
multi-scale nature of big biomedical data that we discussed in the DSPA Motivation setion. Previously, in Chapter 3, we saw space-time (4D) functional magnetic resonance imaging (fMRI) data, and in Chapter 5 we discussed streaming data, which also has a natural temporal dimension.

In this Chapter, we will expand our predictive data analytic
strategies specifically for analyzing longitudinal data. We will
interrogate datasets that track the same type of information, for same
subjects, units or locations, over a period of time. Specifically, we
will present time series analysis, forecasting using autoregressive
integrated moving average (ARIMA) models, structural equation models
(SEM), and longitudinal data analysis via linear mixed models.

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