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Skip to Search Results- 7Frei, Christoph (Mathematical and Statistical Sciences)
- 7Kong, Linglong (Mathematical and Statistical Sciences)
- 7Lewis, Mark (Mathematical and Statistical Sciences)
- 6Han, Bin (Mathematical and Statistical Sciences)
- 6Hillen, Thomas (Mathematical and Statistical Sciences)
- 6Mizera, Ivan (Mathematical and Statistical Sciences)
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Fall 2010
Missing data is always a problem when it comes to data analysis. This is especially the case in anthropology when sex determination is one of the primary goals for fossil skull data since many measurements were not available. We expect to find a classifier that can handle the large amount of...
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Spring 2016
Flows on normed spaces can be classified using flow equivalences --- maps on the space with the property that the structure of one flow is converted into the structure of another flow. Of particular interest are classifications that arise from flow equivalences that are either homeomorphisms or...
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Fall 2016
Survival data is mostly analyzed using Cox proportional hazards model to identify factors associated with survival time of patients. However recently random survival forest (RSF), a non-parametric method for ensemble estimation constructed by bagging of classification trees for survival data, is...
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Spring 2019
Let $G$ be a linear algebraic group defined over a ground field $k$, and let $\mu$ be a $\Gal(k^\sep/k)$-module.A \tb{cohomological invariant} is a morphism $a:H^1(-,G)\to H^n(-,\mu)$ of two functors from the category of field extensions over $k$ to the category of setswhere $H^1(-,G)$ is the...
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Spring 2012
A number of interesting subjects relevant to optimality of design, cost efficiency evaluation, and the adaptive treatment allocation for response-adaptive repeated measurement designs have been reviewed and discussed. First we introduce some optimal crossover designs, and compare those designs...
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Fall 2013
Various analytical methods are available to analyze repeated measures data for both continuous and discrete data. In the case of discrete data, most methods are based on the assumption of asymptotic normality, requiring large samples. Naturally, their small sample performance may not match the...
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Comparison of Sleep State Classification Performance Using Random Forests, Hidden Markov Models, and Non-homogeneous Hidden Markov Models
DownloadFall 2020
In this work, the CF00N polysomnograph data of 75 patients, with ranging severeties of Obstructive Sleep Apnea (OSA), is presented and analyzed in terms of sleep state classification. The pre-processing and cleaning of each polysomnograph recording were performed in R (R Core Team, 2019) using...