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Micah G. Pedrick

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Complexity of Linear Summary Statistics

Michael E. Orrison
Second Reader(s)
Nicholas J. Pippenger


Families of linear functionals on a vector space that are mapped to each other by a group of symmetries of the space have a significant amount of structure.

This results in computational redundancies which can be used to make computing the entire family of functionals at once more efficient than applying each in turn.

This thesis explores asymptotic complexity results for a few such families: contingency tables and unranked choice data.

These are used to explore the framework of Radon transform diagrams, which promise to allow general theorems about linear summary statistics to be stated and proved.

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