Comparative simulation study for model adequancy with binary response variable under multicollinearity – nonparametric approaches

Regression models used to explore the importance of several explanatory variables in estimation, classification and analytical tools play an efficient role for many data analysis. Although the classical linear model is quite easy to use, it is often not sufficient for many real data sets as the relationships between variables do not hold the assumption of the linearity of the relationship between dependent and explanatory variables. Under this study, a nonparametric model fitting that does not require to form a strict mathematical relationship between dependent and explanatory variables will be discussed on the contrary the assumption in multiple linear regression. In this study, the relationship between a binary dependent variable and the explanatory variables will be examined in a conducted simulation study by using generalized linear, the additive logistic regression in case of classical logistic regression model and decision trees to explore the cause and effect relationship. The methods in question and the simulation study will be performed for small, medium and large data sets when multicollinearity problem exists and will be compared with each other.