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Quadratic inference function with ridge estimator for myopic regret-regression: The short-term strategy in optimal dynamic treatment regimes / Nur Raihan Abdul Jalil

A dynamic treatment regime (DTR) is a multi-stage decision rule based on treatment history. The focus in this research is on the improvement of estimation in optimal dynamic treatment regime (ODTR). This research is motivated by the regret-regression method where it is a combination of the regret...

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Main Author: Nur Raihan , Abdul Jalil
Format: Thesis
Published: 2022
Subjects:
Q Science (General)
QA Mathematics
Online Access:http://studentsrepo.um.edu.my/14734/1/Nur_Raihan.pdf
http://studentsrepo.um.edu.my/14734/2/Nur_Raihan.pdf
http://studentsrepo.um.edu.my/14734/
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spelling my.um.stud.147342024-01-21T22:08:33Z Quadratic inference function with ridge estimator for myopic regret-regression: The short-term strategy in optimal dynamic treatment regimes / Nur Raihan Abdul Jalil Nur Raihan , Abdul Jalil Q Science (General) QA Mathematics A dynamic treatment regime (DTR) is a multi-stage decision rule based on treatment history. The focus in this research is on the improvement of estimation in optimal dynamic treatment regime (ODTR). This research is motivated by the regret-regression method where it is a combination of the regret function with regression modeling. A short-term strategy called the myopic regret-regression (MRr) is an alternative to regret-regression where it estimates the mean response at each time-point. This strategy has the same performance as regret-regression but MRr calculation is faster in estimation and more practical in application. However, it has a limitation on correlated data. The quadratic inference functions in myopic regret-regression (QIF-MRr) has overcome the limitation of MRr by combining the myopic regret-regression with quadratic inference functions. It is more robust and efficient regardless of any type of working correlation structure. However, singularity problem happen when estimating the parameters using QIF-MRr and more complex in computations. Hence, the ridge quadratic inference functions for myopic regret-regression (rQIF-MRr) is proposed where a ridge estimator is used to overcome the computational problem and shorten the calculation time. Comparison between methods was performed to check the efficiency and consistency in estimation using simulation with different sample sizes. 2022-04 Thesis NonPeerReviewed application/pdf http://studentsrepo.um.edu.my/14734/1/Nur_Raihan.pdf application/pdf http://studentsrepo.um.edu.my/14734/2/Nur_Raihan.pdf Nur Raihan , Abdul Jalil (2022) Quadratic inference function with ridge estimator for myopic regret-regression: The short-term strategy in optimal dynamic treatment regimes / Nur Raihan Abdul Jalil. Masters thesis, Universiti Malaya. http://studentsrepo.um.edu.my/14734/
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Student Repository
url_provider http://studentsrepo.um.edu.my/
topic Q Science (General)
QA Mathematics
spellingShingle Q Science (General)
QA Mathematics
Nur Raihan , Abdul Jalil
Quadratic inference function with ridge estimator for myopic regret-regression: The short-term strategy in optimal dynamic treatment regimes / Nur Raihan Abdul Jalil
description A dynamic treatment regime (DTR) is a multi-stage decision rule based on treatment history. The focus in this research is on the improvement of estimation in optimal dynamic treatment regime (ODTR). This research is motivated by the regret-regression method where it is a combination of the regret function with regression modeling. A short-term strategy called the myopic regret-regression (MRr) is an alternative to regret-regression where it estimates the mean response at each time-point. This strategy has the same performance as regret-regression but MRr calculation is faster in estimation and more practical in application. However, it has a limitation on correlated data. The quadratic inference functions in myopic regret-regression (QIF-MRr) has overcome the limitation of MRr by combining the myopic regret-regression with quadratic inference functions. It is more robust and efficient regardless of any type of working correlation structure. However, singularity problem happen when estimating the parameters using QIF-MRr and more complex in computations. Hence, the ridge quadratic inference functions for myopic regret-regression (rQIF-MRr) is proposed where a ridge estimator is used to overcome the computational problem and shorten the calculation time. Comparison between methods was performed to check the efficiency and consistency in estimation using simulation with different sample sizes.
format Thesis
author Nur Raihan , Abdul Jalil
author_facet Nur Raihan , Abdul Jalil
author_sort Nur Raihan , Abdul Jalil
title Quadratic inference function with ridge estimator for myopic regret-regression: The short-term strategy in optimal dynamic treatment regimes / Nur Raihan Abdul Jalil
title_short Quadratic inference function with ridge estimator for myopic regret-regression: The short-term strategy in optimal dynamic treatment regimes / Nur Raihan Abdul Jalil
title_full Quadratic inference function with ridge estimator for myopic regret-regression: The short-term strategy in optimal dynamic treatment regimes / Nur Raihan Abdul Jalil
title_fullStr Quadratic inference function with ridge estimator for myopic regret-regression: The short-term strategy in optimal dynamic treatment regimes / Nur Raihan Abdul Jalil
title_full_unstemmed Quadratic inference function with ridge estimator for myopic regret-regression: The short-term strategy in optimal dynamic treatment regimes / Nur Raihan Abdul Jalil
title_sort quadratic inference function with ridge estimator for myopic regret-regression: the short-term strategy in optimal dynamic treatment regimes / nur raihan abdul jalil
publishDate 2022
url http://studentsrepo.um.edu.my/14734/1/Nur_Raihan.pdf
http://studentsrepo.um.edu.my/14734/2/Nur_Raihan.pdf
http://studentsrepo.um.edu.my/14734/
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score 13.222552

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