Parametric versus non-parametric

Linear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the function that describes the relationship between the response and explanatory variables. In many situations, that relationship is not known.

Nonparametric regression differs from parametric regression in that the shape of the functional relationships between the response (dependent) and the explanatory (independent) variables are not predetermined but can be adjusted to capture unusual or unexpected features of the data. When the relationship between the response and explanatory variables is known, parametric regression models should be used. If the relationship is unknown and nonlinear, nonparametric regression models should be used. In case we know the relationship between the response and part of explanatory variables and do not know the relationship between the response and the other part of explanatory variables we use semi-parmetric regression models.