Estimation in regret-regression using quadratic inference functions with ridge estimator

Nur Raihan Abdul Jalil; Nur Anisah Mohamed; Rossita Mohamad Yunus

doi:10.1371/journal.pone.0271542

Estimation in regret-regression using quadratic inference functions with ridge estimator

PLoS One. 2022 Jul 21;17(7):e0271542. doi: 10.1371/journal.pone.0271542. eCollection 2022.

Authors

Nur Raihan Abdul Jalil¹, Nur Anisah Mohamed¹, Rossita Mohamad Yunus¹

Affiliation

¹ Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia.

Abstract

In this paper, we propose a new estimation method in estimating optimal dynamic treatment regimes. The quadratic inference functions in myopic regret-regression (QIF-MRr) can be used to estimate the parameters of the mean response at each visit, conditional on previous states and actions. Singularity issues may arise during computation when estimating the parameters in ODTR using QIF-MRr due to multicollinearity. Hence, the ridge penalty was introduced in rQIF-MRr to tackle the issues. A simulation study and an application to anticoagulation dataset were conducted to investigate the model's performance in parameter estimation. The results show that estimations using rQIF-MRr are more efficient than the QIF-MRr.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Computer Simulation
Data Interpretation, Statistical
Emotions*

Grants and funding

This research is funded by Universiti Malaya www.um.edu.my, Research Grant, (GPF083B-2020 and BKS073-2017 to NAM). The funders had no role in the study design, data collection, and analysis, decision to publish, or preparation of the manuscript.