Shape-constrained regression for nonparametric longitudinal and flexible parametric modelling

Kenyon Ng

Research output: ThesisMaster's Thesis

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This thesis investigates shape-constrained regression methodologies for both nonparametric longitudinal models and parametric models with complex constraints. A novel methodology for fitting Bayesian penalised splines with flexible subject-­specific curves is introduced. We first consider the models in an unconstrained setting before extending them to impose a system of linear inequality constraints on the regression coefficients. Furthermore, we develop a generic constrained regression algorithm that only relies on an indicator function that assesses whether or not the constraints are fulfilled, thus allowing the enforcement of various complex constraints by specifying an appropriate indicator function without altering other parts of- the algorithm.
Original languageEnglish
Awarding Institution
  • The University of Western Australia
Thesis sponsors
Award date15 Nov 2019
Publication statusUnpublished - 2019

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