A More Accurate Estimation of Semiparametric Logistic Regression

Xia Zheng, Yaohua Rong, Ling Liu and Weihu Cheng
Additional contact information
Xia Zheng: Faculty of Science, College of Statistics and Data Science, Beijing University of Technology, Beijing 100124, China
Yaohua Rong: Faculty of Science, College of Statistics and Data Science, Beijing University of Technology, Beijing 100124, China
Ling Liu: Faculty of Science, College of Statistics and Data Science, Beijing University of Technology, Beijing 100124, China
Weihu Cheng: Faculty of Science, College of Statistics and Data Science, Beijing University of Technology, Beijing 100124, China

Mathematics, 2021, vol. 9, issue 19, 1-12

Abstract: Growing interest in genomics research has called for new semiparametric models based on kernel machine regression for modeling health outcomes. Models containing redundant predictors often show unsatisfactory prediction performance. Thus, our task is to construct a method which can guarantee the estimation accuracy by removing redundant variables. Specifically, in this paper, based on the regularization method and an innovative class of garrotized kernel functions, we propose a novel penalized kernel machine method for a semiparametric logistic model. Our method can promise us high prediction accuracies, due to its capability of flexibly describing the complicated relationship between responses and predictors and its compatibility of the interactions among the predictors. In addition, our method can also remove the redundant variables. Our numerical experiments demonstrate that our method yields higher prediction accuracies compared to competing approaches.

Keywords: logistic model; kernel machine; variable selection; semiparametric model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/19/2376/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/19/2376/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:19:p:2376-:d:642629

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().