In order to investigate a series of data scenarios and determine the model governing the changes of a random variable over time, according to the variables affecting it, efficient methods have been developed in recent decades. One of these methods is the generalized additive model. By this modeling for data, it is possible to check the behavior of the non-linear data and even predict the future. In this article, we intend to express this method non-parametrically, in cases such as when the variable is independent, time series, or has a lag and implement the estimation of model parameters. Moreover, we will demonstrate the power and effectiveness of this method by presenting some examples.
Malekzadeh, A., & Rajabi Naraki, F. (2022). Introduction to non-parametric generalized additive models. Journal of Statistical Modelling: Theory and Applications, 3(2), 15-29. doi: 10.22034/jsmta.2023.19520.1083
MLA
Ahad Malekzadeh; Fatemeh Rajabi Naraki. "Introduction to non-parametric generalized additive models", Journal of Statistical Modelling: Theory and Applications, 3, 2, 2022, 15-29. doi: 10.22034/jsmta.2023.19520.1083
HARVARD
Malekzadeh, A., Rajabi Naraki, F. (2022). 'Introduction to non-parametric generalized additive models', Journal of Statistical Modelling: Theory and Applications, 3(2), pp. 15-29. doi: 10.22034/jsmta.2023.19520.1083
VANCOUVER
Malekzadeh, A., Rajabi Naraki, F. Introduction to non-parametric generalized additive models. Journal of Statistical Modelling: Theory and Applications, 2022; 3(2): 15-29. doi: 10.22034/jsmta.2023.19520.1083
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