- Title
- Longitudinal data modelling using penalized splines and ranked set sampling
- Creator
- Al Kadiri, Mohammad
- Date
- 2012
- Type
- Text; Thesis; PhD
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/164930
- Identifier
- vital:13124
- Identifier
- https://library.federation.edu.au/record=b1736123
- Abstract
- "Longitudinal studies, where data is collected by measuring the same experimental units several times over a relatively long period, are becoming increasingly common. Conventional statistical approaches have limitations when applied to the analysis of longitudinal data ... Practical limitations of longitudinal analysis that relate to missing data and large data set sizes were explored in this thesis with the application of a sampling technique known as Ranked Set Sampling (RSS). We developed this sampling method, which has not previously been applied to longitudinal data, for fixed and mixed-effects models. This thesis also illustrated inference techniques to estimate these models after selecting sample units by RSS."; Doctor of Philosophy
- Publisher
- Federation University Australia
- Rights
- This metadata is freely available under a CCO license
- Subject
- Sampling (Statistics); Longitudinal method
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