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In particular, classes of moving average error processes are studied.
In particular, we consider the cases of short and long range error processes.
Its clinical use is, however, largely dictated by trial and error processes.
The design of neural network model is based on trial and error processes and requires past experience for successful implementation.
Finally, some simulation studies are conducted for a fractional autoregressive integrated moving average and for an Ornstein Uhlenbeck error processes.
Econometric models for panel data with spatial error processes have been proposed by Anselin (2001), Kapoor et al. (2007), and Baltagi et al. (2013).
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This duration has been obtained by trial and error process.
New valve development is still a trial and error process.
When the program is moved to a production machine, the trial and error process begins again.
Neural networks operate using what essentially amounts to a very sophisticated trial and error process, eventually arriving at an answer.
Following Baltagi et al. [24], equation two decomposes the error process into a summation of two components; time variant and remainder error process.
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