Michael Lipsitz, Alexandre Belloni, Victor Chernozhukov, Iván Fernández-Val
arXiv 26 Oct 2016 · Statistics — Computation · publishedThe R Journal (2016) · 5 citations (OpenAlex)
arXiv:1610.08329 · PDF · DOI · OpenAlex · Extracted main text
The R package quantreg.nonpar implements nonparametric quantile regression methods to estimate and make inference on partially linear quantile models. quantreg.nonpar obtains point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. It also provides pointwise and uniform confidence intervals over a region of covariate values and/or quantile indices for the same functions using analytical and resampling methods. This paper serves as an introduction to the package and displays basic functionality of the functions contained within.
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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 | A. Belloni, V. Chernozhukov, D. Chetverikov, and I. Fernandez-Val (2011) Conditional Quantile Processes based on Series or Many Regressors | 0.811 | 4 | 2 | 100% |
| 2 | R. Koenker (2011) Additive models for quantile regression: Model selection and confidence bandaids | 0.644 | 2 | 2 | 100% |
| 3 | I. Charlier, D. Paindaveine, and J. Saracco (2015) QuantifQuantile: Estimation of Conditional Quantiles using Optimal Quantization, 2015 | 0.405 | 1 | 1 | 100% |
| 4 | V. Chernozhukov, I. Fernández-Val, and A. Galichon (2009) Improving point and interval estimators of monotone functions by rearrangement | 0.405 | 1 | 1 | 100% |
| 5 | V. Chernozhukov, I. Fernández-Val, and A. Galichon (2010) Quantile and probability curves without crossing | 0.405 | 1 | 1 | 100% |
| 6 | J. O. Ramsay, H. Wickham, S. Graves, and G. Hooker (2014) fda: Functional Data Analysis, 2014 | 0.405 | 1 | 1 | 100% |
| 7 | R. Koenker and G. Basset (1978) Regression quantiles | 0.405 | 1 | 1 | 100% |
| 8 | R. Koenker (2016) quantreg: Quantile Regression, 2016 | 0.405 | 1 | 1 | 100% |
| 9 | V. Muggeo, M. Sciandra, A. Tomasello, and S. Calvo (2013) Estimating growth charts via nonparametric quantile regression: a practical framework with application in ecology | 0.405 | 1 | 1 | 100% |
Showing the top 9 of 9 scored citations.