Introduction to non-parametric generalized additive models

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.

Keywords:

Distributed lag models , Generalized Additive Model , Generalized linear model , Penalized likelihood , Smooth function , Splines