Bayesian semiparametric regression

Regression analysis is performed in order to determine the relationship between two or more variables that have a cause-effect relationship and to make predictions about that subject. These estimates lose their quality of being good estimates when the assumptions of the parametric regression analysis are not met. In this case, nonparametric and semiparametric regression, which are regression methods that allow these assumptions to be stretched, are needed in order to make better predictions. Unlike parametric regression analysis, these methods do not seek linearity, but smoothness. In statistics, two different philosophies are used when making predictions. These are the Classical approach and the Bayesian approach. While parameters are treated as constant variables in the classical approach, in the Bayesian approach, parameters are treated as random variables and are based on Bayes' theorem. Semiparametric regression estimation with Bayesian approach is considered as an alternative method.