Characterizes an estimation-prediction tradeoff in binary logistic models for causal probabilistic temporal graphs and proposes a framework to jointly evaluate temporal link prediction with causal parameter recovery via Cramér-Rao bounds.
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2026 3verdicts
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The IM interval is the shortest valid prior-free procedure for the Behrens-Fisher problem, established via cylindrical predictive random sets, minimaxity, admissibility, and a projection argument.
Redefining the likelihood on the model family M rather than the parameter space causes the strong likelihood principle to collapse into the weak likelihood principle.
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Estimation--Prediction Tradeoff in Causal Probabilistic Temporal Graphs
Characterizes an estimation-prediction tradeoff in binary logistic models for causal probabilistic temporal graphs and proposes a framework to jointly evaluate temporal link prediction with causal parameter recovery via Cramér-Rao bounds.
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Revisiting the Behrens-Fisher Problem: Validity-First Optimality
The IM interval is the shortest valid prior-free procedure for the Behrens-Fisher problem, established via cylindrical predictive random sets, minimaxity, admissibility, and a projection argument.
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Strong Likelihood Principle: Strengthening a Principle or Misunderstanding the Likelihood Function
Redefining the likelihood on the model family M rather than the parameter space causes the strong likelihood principle to collapse into the weak likelihood principle.