Matias D. Cattaneo, Rajita Chandak, Michael Jansson, Xinwei Ma
arXiv 21 Apr 2022 · Mathematics — Statistics Theory · publishedBernoulli (2024) · 10 citations (OpenAlex)
arXiv:2204.10359 · PDF · DOI · OpenAlex · Extracted main text
We begin by introducing a class of conditional density estimators based on local polynomial techniques. The estimators are boundary adaptive and easy to implement. We then study the (pointwise and) uniform statistical properties of the estimators, offering characterizations of both probability concentration and distributional approximation. In particular, we establish uniform convergence rates in probability and valid Gaussian distributional approximations for the Studentized t-statistic process. We also discuss implementation issues such as consistent estimation of the covariance function for the Gaussian approximation, optimal integrated mean squared error bandwidth selection, and valid robust bias-corrected inference. We illustrate the applicability of our results by constructing valid confidence bands and hypothesis tests for both parametric specification and shape constraints, explicitly characterizing their approximation errors. A companion R software package implementing our main results is provided.
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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 | bincollection[author] Giné, EvaristE., Lataa, RafaR. Zinn, JoelJ (2000) ) | 0.737 | 3 | 2 | 100% |
| 2 | barticle[author] Chernozhukov, VictorV., Chetverikov, DenisD. Kato,… (2014) b) | 0.405 | 1 | 1 | 100% |
| 3 | bbook[author] Fan, JianqingJ. Gijbels, IreneI (1996) ) | 0.405 | 1 | 1 | 100% |
| 4 | barticle[author] Chernozhukov, VictorV., Chetverikov, DenisD., Kato,… (2022) ) | 0.405 | 1 | 1 | 100% |
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