Kernel Debiased Plug-in Estimation based on the Universal Least Favorable Submodel
We propose ULFS-KDPE, a kernel debiased plug-in estimator based on the universal least favorable submodel, for estimating pathwise differentiable parameters in nonparametric models. The method constructs a data-adaptive debiasing flow in a reproducing kernel Hilbert space (RKHS), producing a plug-in estimator that achieves semiparametric efficiency without requiring explicit derivation or evaluation of efficient influence functions. We place ULFS-KDPE on a rigorous functional-analytic foundation by formulating the universal least favorable update as a nonlinear ordinary differential equation on probability densities. We establish existence, uniqueness, stability, and finite-time convergen...
| Authors | Haiyi Chen, Yang Liu, Ivana Malenica |
|---|---|
| Primary category | math.ST |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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