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

Authors: Rongzhi Wu; Li He; Lei Peng; Zepeng Wang; Weigang Wang

Addresses: Department of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, Zhejiang Province, 310018, China ' Department of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, Zhejiang Province, 310018, China ' Department of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, Zhejiang Province, 310018, China ' Department of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, Zhejiang Province, 310018, China ' Department of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, Zhejiang Province, 310018, China

Abstract: 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.

Keywords: Lasso model; prior information; sparse framework; variable selection; penalisation; calculation method.

DOI: 10.1504/IJCSM.2021.114190

International Journal of Computing Science and Mathematics, 2021 Vol.13 No.1, pp.42 - 53

Received: 17 Mar 2020
Accepted: 17 Apr 2020
Published online: 13 Apr 2021 *

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