Risk Assessment of Complex Evolving Systems Involving Multiple Inputs

  • A. G. RigasEmail author
  • V. G. Vassiliadis
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 136)


When monitoring complex evolving systems a question that often arises is to detect causal chains of events. Do particular inputs force the system to produce new events or prohibits them? This can be also considered as a risk assessment for the systems response. In this work we present two methods of estimating the effect of multiple inputs on a complex neurophysiological system. Both the response and the stimuli are very long binary time series. The first approach is a non-parametric one and describes the linear and the non-linear association of the stationary point processes by estimating the second- and third-order cumulant density functions. The second approach is a parametric one and the association between the inputs and the response of the system is described by a logistic regression model which takes into account the system’s internal processes.


Cumulant densities Muscle spindle Penalized likelihood function Periodogram Randomized quantile residuals Stationary point process 

MSC 2010:

60G55 62M15 62F12 62G20 92C55 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringDemocritus University of ThraceXanthiGreece

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