The article introduces a new method for estimating complex regression models using kernel density techniques. By applying this method, researchers were able to accurately estimate unknown density functions in multivariate regression models. They also developed a way to test the significance of certain components in the model. Through simulations and real-world data analysis, it was found that the new kernel density estimator outperformed traditional methods, especially for models with multiple peaks or heavy-tailed errors.