Lipschitz functions decompose into monotonic plus linear parts, yielding sample-split estimators with convergence guarantees under heteroscedastic/heavy-tailed errors and adaptivity to unknown function complexity.
Here, the probability measure µ is defined over [0, 1] and µ ([a,b ]) is = ∫b aµ (x) for 0 ≤ a<b ≤
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ME 1years
2023 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
From Isotonic to Lipschitz Regression: A New Interpolative Perspective on Shape-restricted Estimation
Lipschitz functions decompose into monotonic plus linear parts, yielding sample-split estimators with convergence guarantees under heteroscedastic/heavy-tailed errors and adaptivity to unknown function complexity.