Where statistical simulation is defined in quite general terms as a method that utilizes sequences of random numbers to conduct the simulation, we can loosely determine them as a statistical simulation method.
In this paper, the use of statistical simulation tools is proposed in order to test the significance of the difference between the accuracy of two prediction methods: regression and estimation by analogy.
The parameters that are used in the statistical simulation with the Monte Carlo method are listed in Table 1.
The proposed method is examined using a statistical simulation and is further validated in an experiment that involved two vehicles in a holding pattern scenario.
Another important parameter in the statistical simulation is the correlation.
A crucial component in the statistical simulation of a computationally expensive model is a good design of experiments.
The gained statistical simulation results enabled 50% reduction in the hardware overhead of the implemented memory scrubbing technique without loss in fault-tolerance.
The data of this statistical simulation strongly supported that apoptosis in TCs was directly controlled by the apoptotic status of the neighbouring IFC (see legend of Fig. 4 for details).
The "true" data were then used in the statistical simulation of model data with the inclusion of both classical and Berkson-like error.
With two M+ and M− arrays, we can think of both intra-array correlation and inter-array correlation in performing the statistical simulation.
