Equations (4) and (5) can be used to model nonlinear relationships between a set of input and output variables.
This imparts great flexibility for modelling nonlinear relationships in real data.
For example, linear discriminant analysis assumes a linear additive relationship among the variables or attributes while support vector machine or neural network can model nonlinear relationships.
Table 1 shows an example of data points used in the study.> ANN models can be used to model nonlinear relationships.
Fourth, given the high dimensions, computationally expensive methods, for example, principal curves [ 13, 14], are hard to be adopted even though they can model nonlinear relationships.
The generalization is motivated by modelling nonlinear relationships, while the simplification is justified because AR can be used to approximate MA or ARMA.
Models were developed either on the basis of standard assumptions of log linear or step functional relationships with survival, or a novel approach based on nonlinear relationships, using more complex fractional polynomial (FP) transformations: a flexible, parametric method for modelling nonlinear relationships (Royston and Altman, 1994; Altman and Lyman, 1998).
FPs provide a flexible, parametric approach for modelling nonlinear relationships, making full use of the information available within each variable and as such can provide a clearer insight into the nature of the underlying relationship (Royston and Altman, 1994; Altman and Lyman, 1998).
Finally, while it is beyond the scope of this paper to discuss more complex regression methods, an even more rigorous approach could use generalized linear models to model nonlinear relationships between student characteristics and test scores, analyze student responses at the individual-question level, or produce unbiased estimates in the presence of a ceiling effect.
In the literature, artificial neural networks are employed to model nonlinear relationship between the inputs and the outputs of a given system.
