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The finite sample properties of the proposed estimation algorithm are studied under various lag structures and correlation levels among the predictor processes in simulation studies.
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Consider the process in simulation experiment 1.
Note that, small number of replicates (3 times) was due to the huge computational resource required for each process in simulation.
Thinking aloud techniques enable a fuller exploration of the decision-making process in simulation scenarios capturing what is held in the short-term memory [ 20].
More detailed reporting, however, can establish an explicit outline of the decision-making process in simulation-based analysis for enhanced reproducibility, reusability, and sharing.
Our observations of rate variation across branches, despite a clock-like substitution process in simulations, are consistent with previously suggested sources of error in rate estimation.
Considering the process described in simulation experiment 2, 100 subgroups are first randomly generated from the in-control process.
We will be using fuzzy numbers for process times in simulation model.
In order to include these processes in numerical simulations a micromechanical model needs to be coupled to the finite element solution.
They did not include any ionospheric processes in their simulations.
These time courses are representative of the processes in all simulations in this paper.
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