Regression analysis is a powerful tool to describe the relationship between response and predictors. In practice, parametric regression models such as linear regression models are widely used due to their simplicity and interpretability. When a parametric regression model is correctly fitted by the data, further statistical analysis can be easily and accurately elaborated with good explanations. However, such further analysis and interpretation could be misleading when the model does not fit the data well. A practical example is production theory in economics, in which the Cobb-Douglas function is commonly used to describe the linear relationship between the login puts, such as labor and capital, and the log-output. However, this function may not well describe the relationship. To avoid model mis-specification, nonparametric regression models are developed. The models are very °exifble, but loss some interpretability.