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Full-Text Articles in Medicine and Health Sciences
"A Comparison Of Variable Selection Methods Using Bootstrap Samples From Environmental Metal Mixture Data", Paul-Yvann Djamen
"A Comparison Of Variable Selection Methods Using Bootstrap Samples From Environmental Metal Mixture Data", Paul-Yvann Djamen
Mathematics & Statistics ETDs
In this thesis, I studied a newly developed variable selection method SODA, and three customarily used variable selection methods: LASSO, Elastic net, and Random forest for environmental mixture data. The motivating datasets have neuro-developmental status as responses and metal measurements and demographic variables as covariates. The challenges for variable selections include (1) many measured metal concentrations are highly correlated, (2) there are many possible ways of modeling interactions among the metals, (3) the relationships between the outcomes and explanatory variables are possibly nonlinear, (4) the signal to noise ratio in the real data may be low. To compare these methods …
An Improved Method For Spectroscopic Quality Classification, Elizabeth G. Mayer
An Improved Method For Spectroscopic Quality Classification, Elizabeth G. Mayer
Mathematics & Statistics ETDs
Spectral quality classification is a vital step in data cleaning before the
analysis of magnetic resonance spectroscopy (MRS) data can be done. This
analysis compares five methods of quality classification; three of these are
legacy methods, Maudsley et al. (2006), Zhang et al. (2018), and
Bustillo et al. (2020), and two newly created methods that used a random forests
classifier (RFC) to inform their classifications. We found that the random forest
classifier was the most accurate at predicting spectra quality (balanced
accuracy for RF of 88% vs legacy of 70%, 72%, or 72%). A
Random-Forests-Informed Filtering method (RFIFM) for quality …