Matias D. Cattaneo, Max H. Farrell, Yingjie Feng
arXiv 1 Jun 2019 · Statistics — Computation · publishedThe R Journal (2020) · 3 citations (OpenAlex)
arXiv:1906.00202 · PDF · DOI · OpenAlex · Extracted main text
Nonparametric partitioning-based least squares regression is an important tool in empirical work. Common examples include regressions based on splines, wavelets, and piecewise polynomials. This article discusses the main methodological and numerical features of the R software package lspartition, which implements modern estimation and inference results for partitioning-based least squares (series) regression estimation. This article discusses the main methodological and numerical features of the R software package lspartition, which implements results for partitioning-based least squares (series) regression estimation and inference from Cattaneo and Farrell (2013) and Cattaneo, Farrell, and Feng (2019). These results cover the multivariate regression function as well as its derivatives. First, the package provides data-driven methods to choose the number of partition knots optimally, according to integrated mean squared error, yielding optimal point estimation. Second, robust bias correction is implemented to combine this point estimator with valid inference. Third, the package provides estimates and inference for the unknown function both pointwise and uniformly in the conditioning variables. In particular, valid confidence bands are provided. Finally, an extension to two-sample analysis is developed, which can be used in treatment-control comparisons and related 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 | M. D. Cattaneo, M. H. Farrell, and Y. Feng (2019) Large sample properties of partitioning-based estimators self | 1.000 | 17 | 5 | 100% |
| 2 | M. D. Cattaneo and M. H. Farrell (2013) Optimal convergence rates, bahadur representation, and asymptotic normality of partitioning estimators self | 0.644 | 2 | 2 | 100% |
| 3 | S. Calonico, M. D. Cattaneo, and M. H. Farrell (2018) On the effect of bias estimation on coverage accuracy in nonparametric inference self | 0.405 | 1 | 1 | 100% |
| 4 | S. Calonico, M. D. Cattaneo, and M. H. Farrell (2019) Coverage error optimal confidence intervals for local polynomial regression self | 0.405 | 1 | 1 | 100% |
| 5 | C. K. Chui (2016) An Introduction to Wavelets | 0.405 | 1 | 1 | 100% |
| 6 | A. Cohen, I. Daubechies, and P. Vial (1993) Wavelets on the interval and fast wavelet transforms | 0.405 | 1 | 1 | 100% |
| 7 | J. Fan and I. Gijbels (1996) Local Polynomial Modelling and Its Applications | 0.405 | 1 | 1 | 100% |
| 8 | L. Györfi, M. Kohler, A. Krzyżak, and H. Walk (2002) A Distribution-Free Theory of Nonparametric Regression | 0.405 | 1 | 1 | 100% |
| 9 | J. Harezlak, D. Ruppert, and M. P. Wand (2018) Semiparametric Regression with R | 0.405 | 1 | 1 | 100% |
| 10 | J. S. Long and L. H. Ervin (2000) Using heteroscedasticity consistent standard errors in the linear regression model | 0.405 | 1 | 1 | 100% |
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