What question did this study set out to answer?

To generalize eigenvalue sampling algorithms for spiked Wishart and pseudo-Wishart distributions to support an arbitrary number of spikes and enable gradient-based distribution fitting.

April 10, 2025Communications in Statistics - Simulation and ComputationOpen Access

Sampling spiked Wishart eigenvalues

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To generalize eigenvalue sampling algorithms for spiked Wishart and pseudo-Wishart distributions to support an arbitrary number of spikes and enable gradient-based distribution fitting.
Extended existing eigenvalue sampling frameworks from single-spike cases to spiked Wishart and spiked pseudo-Wishart distributions with arbitrary numbers of spikes.
Formulated a differentiation scheme for the sampling process to support stochastic gradient descent optimization.
Achieved efficient, generalized eigenvalue sampling across matrices with multiple spikes without restricting spike counts.
Demonstrated that the differentiated sampling pipeline enables direct fitting of simulated eigenvalue spectra to specified target distributions.

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Implication

Computational study demonstrates efficient sampling of spiked Wishart eigenvalues across arbitrary spikes, enabling gradient-based optimization for target distributions.

Cite This Study

Thomas G. Brooks (2025) studied this question.

synapsesocial.com/papers/6a1bfaf3ea84844e355f4363 https://doi.org/10.1080/03610918.2025.2490204

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Implication

Computational study demonstrates efficient sampling of spiked Wishart eigenvalues across arbitrary spikes, enabling gradient-based optimization for target distributions.

Sampling spiked Wishart eigenvalues

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Sampling spiked Wishart eigenvalues

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