A low-regret online predictor for any GLM yields a valid confidence sequence for the true parameter, giving a unified framework and new sample-size-independent and sparse-model bounds.
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Confidence Sequences for Generalized Linear Models via Regret Analysis
A low-regret online predictor for any GLM yields a valid confidence sequence for the true parameter, giving a unified framework and new sample-size-independent and sparse-model bounds.