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.
37 In step (I) we used the following bound on E[Γn]: E[Γn] = E ( N − 1 N∑ i=1 ξ2 i )1/ 2 + ∥f0∥∞ +F ≤ σ + ∥f0∥∞ +F since E[ξ2 i |Xi]<σ 2
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.