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Explain the comparative power of matrix- vs. scalar-valued test supermartingales

Provide a theoretical explanation for the empirical differences in power observed between matrix-valued and scalar-valued test supermartingales when testing the same sequential null hypotheses, clarifying when and why matrix-based methods are more powerful.

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Background

The empirical section compares matrix-valued and scalar-valued test supermartingales, finding scenarios where matrix-based procedures are more powerful (particularly under non-commuting observations). A theoretical account of these phenomena is lacking, and understanding it could guide the choice of methods in practice.

References

We also leave open some questions for future studies to address. These include a deeper understanding of the matrix randomizer U in our paper, which belongs to the "trace super-uniform" distribution family; tighter p-Chebyshev inequalities; further investigation into matrix-valued e-processes; as well as the theoretical explanation of our comparison between matrix-valued test supermartingales and scalar-valued test supermartingales when testing sequentially the same null hypotheses.

Positive Semidefinite Matrix Supermartingales (2401.15567 - Wang et al., 28 Jan 2024) in Conclusion