Matias D. Cattaneo, Michael Jansson, Xinwei Ma
arXiv 15 Jun 2019 · Statistics — Computation · publishedJournal of Statistical Software (2022) · 20 citations (OpenAlex)
arXiv:1906.06529 · PDF · DOI · OpenAlex · Extracted main text
Density estimation and inference methods are widely used in empirical work. When the underlying distribution has compact support, conventional kernel-based density estimators are no longer consistent near or at the boundary because of their well-known boundary bias. Alternative smoothing methods are available to handle boundary points in density estimation, but they all require additional tuning parameter choices or other typically ad hoc modifications depending on the evaluation point and/or approach considered. This article discusses the R and Stata package lpdensity implementing a novel local polynomial density estimator proposed and studied in Cattaneo, Jansson, and Ma (2020, 2021), which is boundary adaptive and involves only one tuning parameter. The methods implemented also cover local polynomial estimation of the cumulative distribution function and density derivatives. In addition to point estimation and graphical procedures, the package offers consistent variance estimators, mean squared error optimal bandwidth selection, robust bias-corrected inference, and confidence bands construction, among other features. A comparison with other density estimation packages available in R using a Monte Carlo experiment 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 | Cattaneo MD, Jansson M, Ma X (2021) Local Regression Distribution Estimators | 0.874 | 5 | 2 | 100% |
| 2 | Calonico S, Cattaneo MD, Farrell MH (2018) On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference | 0.811 | 4 | 2 | 100% |
| 3 | Calonico S, Cattaneo MD, Farrell MH (2020) Coverage Error Optimal Confidence Intervals for Local Polynomial Regression | 0.811 | 4 | 2 | 100% |
| 4 | Cattaneo MD, Jansson M, Ma X (2020) Simple Local Polynomial Density Estimators | 0.737 | 3 | 2 | 100% |
| 5 | Cheng MY, Fan J, Marron JS (1997) On Automatic Boundary Corrections | 0.644 | 2 | 2 | 100% |
| 6 | Karunamuni RJ, Albert T (2005) On Boundary Correction in Kernel Density Estimation | 0.644 | 2 | 2 | 100% |
| 7 | Zhang S, Karunamuni RJ (1998) On Kernel Density Estimation Near Endpoints | 0.644 | 2 | 2 | 100% |
| 8 | Fan J, Gijbels I (1996) Local Polynomial Modelling and Its Applications | 0.511 | 2 | 1 | 100% |
| 9 | Calonico S, Cattaneo MD, Farrell MH (2019) nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference | 0.405 | 1 | 1 | 100% |
| 10 | Calonico S, Cattaneo MD, Farrell MH (2020) nprobust: Nonparametric Robust Estimation and Inference Methods using Local Polynomial Regression and Kernel Density Estimation | 0.405 | 1 | 1 | 100% |
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