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

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

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.

Suggested Citation

  • Nur Raihan Abdul Jalil & Nur Anisah Mohamed & Rossita Mohamad Yunus, 2022. "Estimation in regret-regression using quadratic inference functions with ridge estimator," PLOS ONE, Public Library of Science, vol. 17(7), pages 1-15, July.
    • Handle: RePEc:plo:pone00:0271542
    DOI: 10.1371/journal.pone.0271542
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    References listed on IDEAS

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