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Skip to Search Results- 477Department of Mathematical and Statistical Sciences
- 2Department of Biological Sciences
- 2Department of Mechanical Engineering
- 1Department of Civil and Environmental Engineering
- 1Department of Computing Science
- 1Department of Public Health Sciences
- 7Frei, Christoph (Mathematical and Statistical Sciences)
- 7Hillen, Thomas (Mathematical and Statistical Sciences)
- 7Kong, Linglong (Mathematical and Statistical Sciences)
- 7Lewis, Mark (Mathematical and Statistical Sciences)
- 6Han, Bin (Mathematical and Statistical Sciences)
- 6Kashlak, Adam (Mathematical and Statistical Sciences)
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Fall 2024
Motivated by Elizabeth Meckes’ work on concentration inequalities using the group SO(n) (see [9]), this thesis explores the use of random rotations for detecting autocorrelation in time series data. Traditional tests like the Durbin Watson test assess autocorrelation by analyzing quadratic forms...
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Fall 2024
We consider the problem of a firm that wants to maximize its earnings. Production generates pollution as a by-product and has a negative impact on the environment. This negative impact causes disutility. The firm determines the optimal production rate and chooses between two types of technologies...
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Fall 2024
Let Cat(oo,oo) denote the (oo,1)-category of (oo,oo)-categories with weakly inductive equivalences. The main objective of this thesis is to demonstrate that Cat(oo,oo) satisfies universal properties with respect to homotopy-coherent internalisation and enrichment. To achieve these universal...
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Fall 2024
In this thesis, we explore the spatial dynamics of viral infection within tissue through mathematical modelling, aiming to understand the impact of virus spread on both cancerous and healthy tissue. Specifically, we investigate how spatial patterning and heterogeneity influence viral infection...
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Fall 2024
We study the Severi varieties of Atiyah ruled surfaces over generic elliptic curves. In particular, we prove that general members of such varieties have at worst ordinary triple points.
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Bayesian hierarchical modeling and its applications to clustering and data privacy preservation
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The evolution of data acquisition technologies and the exponential growth in computing capabilities have inaugurated an epoch wherein researchers are empowered to procure data of unprecedented dimensionality and complexity. Simultaneously, Bayesian hierarchical models distinguish themselves as...
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Statistical Learning and Inference For Functional Predictor Models via Reproducing Kernel Hilbert Space
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Functional regression is a cornerstone for understanding complex relationships where predictors or responses (or both) are functions. A particularly powerful framework within this domain is the Reproducing Kernel Hilbert Space (RKHS), which facilitates the handling of infinite-dimensional data...