Summary

In this chapter, several advanced techniques to solve regression problems that cannot be solved with linear models were treated. First, a nonlinear least squares method was explored, where the parameters of the regression function to be estimated were nonlinear. In this technique, given the nonlinearity of the coefficients, the solution of the problem occurs by means of iterative numerical calculation methods. Then a MARS was performed. This is a nonparametric regression procedure that makes no assumption about the underlying functional relationship between the response and predictor variables. This relationship is constructed from a set of coefficients and basis functions that are processed, starting from the regression data.  

Later, we focused attention on a GAM. This is a GLM in which the linear predictor is given by a user-specified sum of smooth functions of the covariates plus a conventional parametric component of the linear predictor. Then, we introduced the tree regression...