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stat.AP2026
Interpreting relative utility for probabilistic predictions
Linard Hoessly
At a fixed threshold, relative utility (RU) measures the net-benefit gain of a prediction model over the better of treat-all and treat-none relative to the corresponding gain under…
stat.ME2026
Exact two-sided p-values in natural exponential families: coincidence, non-uniqueness, and sample-size stability
Shaul K. Bar-Lev, Linard Hoessly
We study the non-uniqueness of exact two-sided -values in continuous one-parameter natural exponential families (NEFs). For directed one-sided problems, the tail -value agree…
stat.AP2026
Why decision curves go above or below treat-all and treat-none: a PPV- and calibration-based guide for clinical prediction models
Linard Hoessly
Net benefit is widely used to assess whether clinical prediction models can improve decision-making, but its interpretation is often difficult in practice. In this didactic note, w…