Statistical methods for electromyography data and associated problems
Data(s) |
2014
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Resumo |
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. |
Formato |
application/pdf |
Identificador | |
Publicador |
Queensland University of Technology |
Relação |
http://eprints.qut.edu.au/79631/1/James_McKeone_Thesis.pdf McKeone, James P. (2014) Statistical methods for electromyography data and associated problems. PhD thesis, Queensland University of Technology. |
Fonte |
School of Mathematical Sciences; Science & Engineering Faculty |
Palavras-Chave | #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 |
Tipo |
Thesis |