Statistical methods for electromyography data and associated problems

McKeone, James P. (2014) Statistical methods for electromyography data and associated problems. PhD thesis, Queensland University of Technology.

Abstract

This thesis proposes three novel models which extend the statistical methodology for motor unit number estimation, a clinical neurology technique. Motor unit number estimation is important in the treatment of degenerative muscular diseases and, potentially, spinal injury. Additionally, a recent and untested statistic to enable statistical model choice is found to be a practical alternative for larger datasets. The existing methods for dose finding in dual-agent clinical trials are found to be suitable only for designs of modest dimensions. The model choice case-study is the first of its kind containing interesting results using so-called unit information prior distributions.

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72 since deposited on 17 Feb 2015
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ID Code: 79631
Item Type: QUT Thesis (PhD)
Supervisor: Pettitt, Tony, Anh, Vo, & Drovandi, Christopher
Keywords: Motor unit number estimation, Multiplicative spline, Functional data analysis, Markov chain Monte Carlo, Reversible jump, Model Choice, Marginal likelihood, Widely applicable Bayesian information criterion, Phase I clincial trial design, Partial ordering
Divisions: Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Institution: Queensland University of Technology
Deposited On: 17 Feb 2015 06:00
Last Modified: 08 Sep 2015 06:44

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