Research and application of Lasso regression model based on prior coefficient framework

In recent years, the establishment of a suitable data model and data analysis with a few significant features have drawn many scientists' attention. The Lasso model can effectively process high dimensional data and keep the corresponding accuracy. Compared to the traditional regression, the Lasso regression model and its improved model can solve better variable selection. In this paper, a new Lasso improved method is proposed for the Lasso regression model. The prior information is incorporated into the model. This paper refers to the Lasso regression model based on the prior sparse framework and gives the corresponding algorithm. Additionally, it analyses multiple sets of simulation and empirical data. The results show that the improved model has better performance than the traditional model with prior information.