Print  Close

Fast Cost-constrained Regression

Abstract Number:

3181 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

HyeongJin Hyun (1), Xiao Wang (1)

Institutions:

(1) Purdue University, West Lafayette

Co-Author:

Xiao Wang  
Purdue University

First Author:

HyeongJin Hyun  
Purdue University

Presenting Author:

HyeongJin Hyun  
N/A

Abstract Text:

The conventional statistical models assume the availability of covariates without associated costs, yet real-world scenarios often involve acquisition costs and budget constraints imposed on these variables. Scientists must navigate a trade-off between model accuracy and expenditure within these constraints. In this paper, we introduce fast cost-constrained regression (FCR), designed to tackle such problems with computational and statistical efficiency. Specifically, we develop fast and efficient algorithms to solve cost-constrained problems with the loss function satisfying a quadratic majorization condition. We theoretically establish nonasymptotic error bounds for the algorithm's solution, considering both estimation and selection accuracy. We apply FCR to extensive numerical simulations and four datasets from the National Health and Nutrition Examination Survey. Our method outperforms the latest approaches in various performance measures, while requiring fewer iterations and a shorter runtime.

Keywords:

budget constraints|cost|high dimensional regression|non-convex optimiztion| |

Sponsors:

Biometrics Section

Tracks:

Model/Variable Selection

Can this be considered for alternate subtype?

Yes

Are you interested in volunteering to serve as a session chair?

No

I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.

I understand