Non-Euclidean Conditional Expectation and Filtering

Anastasis Kratsios; Cody B. Hyndman

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arXiv · 1710.05829

Non-Euclidean Conditional Expectation and Filtering

Anastasis Kratsios·Cody B. Hyndman

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Abstract

A non-Euclidean generalization of conditional expectation is introduced and characterized as the minimizer of expected intrinsic squared-distance from a manifold-valued target. The computational tractable formulation expresses the non-convex optimization problem as transformations of Euclidean conditional expectation. This gives computationally tractable filtering equations for the dynamics of the intrinsic conditional expectation of a manifold-valued signal and is used to obtain accurate numerical forecasts of efficient portfolios by incorporating their geometric structure into the estimates.

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Anastasis Kratsios, Cody B. Hyndman. 2017-10-16. Non-Euclidean Conditional Expectation and Filtering. https://arxiv.org/abs/1710.05829

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