arXiv 28 Dec 2024 · Statistics — Methodology
arXiv:2412.20173 · PDF · DOI · OpenAlex · Extracted main text
This study proposes a debiasing method for smooth nonparametric estimators. While machine learning techniques such as random forests and neural networks have demonstrated strong predictive performance, their theoretical properties remain relatively underexplored. In particular, many modern algorithms lack guarantees of pointwise and uniform risk convergence, as well as asymptotic normality. These properties are essential for statistical inference and robust estimation and have been well-established for classical methods such as Nadaraya-Watson regression. To ensure these properties for various nonparametric regression estimators, we introduce a model-free debiasing method. By incorporating a correction term that estimates the conditional expected residual of the original estimator, or equivalently, its estimation error, into the initial nonparametric regression estimator, we obtain a debiased estimator that satisfies pointwise and uniform risk convergence, along with asymptotic normality, under mild smoothness conditions. These properties facilitate statistical inference and enhance robustness to covariate shift, making the method broadly applicable to a wide range of nonparametric regression problems.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 0.737 | 3 | 2 | 100% |
| 2 | Johannes Schmidt-Hieber and Petr Zamolodtchikov (2024) Local convergence rates of the nonparametric least squares estimator with applications to transfer learning | 0.737 | 3 | 2 | 100% |
| 3 | Victor Chernozhukov, Whitney K. Newey, and Vasilis Syrgkanis (2024) Conditional influence functions, 2024 | 0.737 | 3 | 2 | 100% |
| 4 | Hidehiko Ichimura and Whitney K. Newey (2022) The influence function of semiparametric estimators | 0.644 | 2 | 2 | 100% |
| 5 | Hidetoshi Shimodaira (2000) Improving predictive inference under covariate shift by weighting the log-likelihood function | 0.644 | 2 | 2 | 100% |
| 6 | Edward H. Kennedy (2023) Semiparametric doubly robust targeted double machine learning: a review, 2023 | 0.644 | 2 | 2 | 100% |
| 7 | Heejung Bang and James M. Robins (2005) Doubly robust estimation in missing data and causal inference models | 0.405 | 1 | 1 | 100% |
| 8 | Leo Breiman (2001) Random forests | 0.405 | 1 | 1 | 100% |
| 9 | Peter Bühlmann and Sara van de Geer (2011) Statistics for high-dimensional data | 0.405 | 1 | 1 | 100% |
| 10 | Jana Janková and Sara van de Geer (2018) Semiparametric efficiency bounds for high-dimensional models | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 25 scored citations.