Abstract
According to the feature of high-dimensional data,the number of variables is usually larger than the sample size and the data are often heterogeneous,a robust and effective feature selection method was proposed by using the dimensional reduction technique of variable selection and the modal regression based estimation method.The estimation algorithm was given by using Local Quadratic Algorithm(LQA) and Expectation-Maximum(EM) algorithm,and the selection method of the parameter adjustment was also discussed.Data analysis of the simulation shows that the proposed method is overall better than the least square and median regression based regularized method.Compared with the existing methods,the proposed method has higher prediction ability and stronger robustness especially for the non-normal error distribution.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
