A PLS-Integrated Lasso Method With Application in Index Tracking

Shiqin TANG, Yining DONG, S. Joe QIN

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

Abstract

In traditional multivariate data analysis, dimension reduction and regression have been treated as distinct endeavors. Established techniques such as principal component regression (PCR) and partial least squares (PLS) regression traditionally compute latent components as intermediary steps—although with different underlying criteria—before proceeding with the regression analysis. In this paper, we introduce an innovative regression methodology named PLS-integrated Lasso (PLS-Lasso) that integrates the concept of dimension reduction directly into the regression process. We present two distinct formulations for PLS-Lasso, denoted as PLS-Lasso-v1 and PLS-Lasso-v2, along with clear and effective algorithms that ensure convergence to global optima. PLS-Lasso-v1 and PLS-Lasso-v2 are compared with Lasso on the task of financial index tracking and show promising results.
Original languageEnglish
Title of host publicationICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Pages6520-6524
ISBN (Print)9798350344851
DOIs
Publication statusPublished - Apr 2024
EventICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Seoul, Korea, Republic of
Duration: 14 Apr 202419 Apr 2024

Conference

ConferenceICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Period14/04/2419/04/24

Fingerprint

Dive into the research topics of 'A PLS-Integrated Lasso Method With Application in Index Tracking'. Together they form a unique fingerprint.

Cite this