Abstract
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 language | English |
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Qualification | Masters |
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Award date | 15 Nov 2019 |
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Publication status | Unpublished - 2019 |