Nested model averaging on solution path for high-dimensional linear regression

Yang Feng, Qingfeng Liu

Research output: Contribution to journalArticlepeer-review

Abstract

We study the nested model averaging method on the solution path for a high-dimensional linear regression problem. In particular, we propose to combine model averaging with regularized estimators (e.g., lasso, elastic net, and Sorted L-One Penalized Estimation [SLOPE]) on the solution path for high-dimensional linear regression. In simulation studies, we first conduct a systematic investigation on the impact of predictor ordering on the behaviour of nested model averaging, and then show that nested model averaging with lasso, elastic net and SLOPE compares favourably with other competing methods, including the infeasible lasso, elastic, net and SLOPE with the tuning parameter optimally selected. A real data analysis on predicting the per capita violent crime in the United States shows outstanding performance of the nested model averaging with lasso.

Original languageEnglish (US)
Article numbere317
JournalStat
Volume9
Issue number1
DOIs
StatePublished - 2020

Keywords

  • SLOPE
  • high-dimensional regression
  • lasso
  • model averaging
  • regularization

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'Nested model averaging on solution path for high-dimensional linear regression'. Together they form a unique fingerprint.

Cite this