Simple Local Polynomial Density Estimators
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Abstract (arXiv)
This paper introduces an intuitive and easy-to-implement nonparametric density estimator based on local polynomial techniques. The estimator is fully boundary adaptive and automatic, but does not require pre-binning or any other transformation of the data. We study the main asymptotic properties of the estimator, and use these results to provide principled estimation, inference, and bandwidth selection methods. As a substantive application of our results, we develop a novel discontinuity in density testing procedure, an important problem in regression discontinuity designs and other program evaluation settings. An illustrative empirical application is given. Two companion Stata and R software packages are provided.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Nov 2018.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1; 2025: 0; 2026: 2.
5 papers.
- 2024A Causal Framework for Evaluating Deferring Systemscited by 13
- 2021Boosting in Univariate Nonparametric Maximum Likelihood Estimationcited by 2
- 2023Bayes Risk Consistency of Nonparametric Classification Rules for Spike Trains Datacited by 1
- 2026Compactly supported radial basis functions as probability density functionscited by 0
- 2026Local polynomial density ratio estimationcited by 0
- 2026Local polynomial density ratio estimation29 Sep 2026
- 2026Compactly supported radial basis functions as probability density functions29 Jul 2026
- 2024A Causal Framework for Evaluating Deferring Systems29 May 2024
- 2023Bayes Risk Consistency of Nonparametric Classification Rules for Spike Trains Data9 Aug 2023
- 2021Boosting in Univariate Nonparametric Maximum Likelihood Estimation21 Jan 2021