Thomas Lumley • 12/14/2023

How good is the leading eigenvalue approximation to quadratic forms?

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This technical article evaluates a leading eigenvalue approximation method for computing tail probabilities of quadratic forms in high-dimensional Gaussian variables. It compares this efficient approximation, which requires only the top k eigenvalues, to the full eigendecomposition and the traditional Satterthwaite approximation, demonstrating its superior performance and bounded relative error even for small k.

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#statistics #Numerical Methods #Quadratic Forms
How good is the leading eigenvalue approximation to quadratic forms?

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