The independence model, homogeneous agreement model, and non-homogeneous agreement model form a nested sequence of models.
The transition probability of the Markov chain governed the sequence of models in the chain, which were a subset of the finite number of possible models.
On the basis of the sequence of model selection, two hybrid models (ARIMA-MLP and MLP-ARIMA) are presented in the next section.
So until now, to the best of our knowledge, there is very little research on the limit behavior of the iterative sequence of model (2.1).
The whole parameter sequence is then calculated from that complete sequence of models, taking into account the dynamics of the synthesized parameters thanks to the first and second derivatives of the parameters.
A sequence of models (or states) were developed that adhered to the fundamental Markov chain rule (i.e., the probability of the next model depends only on the current model).
Structural equation modeling (SEM) was used to test a sequence of models that examined the mediating effects of opportunity recognition and innovativeness on the relationships (paths) between risk-taking propensity, entrepreneurial intensity, and firm sustainability (Joreskog and Sorbom 1993).
(For a review of these mechanisms see [17].) It is one of a long sequence of models developed from 1998 to the present, with notable contributions by Markram, [18], and Dittman, Kreitzer and Regehr [19].
The Naive Bayes Classifier model and an Ensemble model using a sequence of models (i.e., Support Vector Machine, K-Nearest Neighbors, and Naive Bayes Classifier) had the best results among the seven tested modeling methods.
A sequence of models was generated by forward selection.
