Improvement And Application Of Elastic Net Method With Prior Information

With the rise of big data-related technologies in recent years and the further development of data collection technology,high-dimensional data has appeared in large numbers in the fields of natural science,biomedicine,and information science.When dealing with high-dimensional data,variable selection is an effective method that can reduce the influence of irrelevant variables to achieve the effect of reducing dimensionality.Among the many variable selection methods,the Elastic Net method can efficiently process high-dimensional data with strongly correlated variable groups and obtain reliable parameter estimates.Therefore,the related research of Elastic Net method has received great attention in the field of statistics.Aiming at the problem of how to improve the Elastic Net method,this article proposes a new improvement method,namely the Elastic Net method based on prior information.Specifically,the prior information is incorporated into the Elastic Net model with the help of a sparse framework,thereby improving the fitting effect of the model.After giving the definition of the method,this paper gives the corresponding solution algorithm at the theoretical level,and proves that the Elastic Net method based on prior information also has the nature of group effect.Afterwards,multiple sets of numerical simulations show that Elastic Net based on prior information has better stability and accuracy than Elastic Net methods.Aiming at the application problem of the Elastic Net method based on prior information,this paper considers the application of the Elastic Net method based on prior information in the field of missing data.Conventional missing data processing methods generally cannot achieve good results when dealing with missing data with a large missing rate.The Elastic Net method based on prior information proposed in this paper can provide another way of thinking.First,extract the prior information from the missing samples,and then use the prior information to improve the fitting effect of the model.At the end of this paper,experiments are carried out on simulated data and real data.The results show that the Elastic Net method based on prior information can indeed use the prior information extracted from missing data to improve the model effect.