Running Average Algorithm Produces Monotonic Envelope
A researcher developing a hybrid reinforcement-learning and gradient-descent optimizer for a gamma-ray observatory reports a statistical issue with running averages while smoothing time-series data. They show that enforcing a monotonically nondecreasing running-average envelope creates multiple-testing bias that flattens slow growth; they propose using significance thresholds and an adaptive k (e.g., k = sqrt(log N)) to reduce false downward updates but note no universal fix exists.
Scoring Rationale
Insightful practical remedy for monotonic averaging, but heuristic single-source analysis and limited empirical validation restricts general applicability.
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