np

Nonparametric Econometrics: The np Package. We describe the R np package via a series of applications that may be of interest to applied econometricians. The np package implements a variety of nonparametric and semiparametric kernel-based estimators that are popular among econometricians. There are also procedures for nonparametric tests of significance and consistent model specification tests for parametric mean regression models and parametric quantile regression models, among others. The np package focuses on kernel methods appropriate for the mix of continuous, discrete, and categorical data often found in applied settings. Data-driven methods of bandwidth selection are emphasized throughout, though we caution the user that data-driven bandwidth selection methods can be computationally demanding.

This software is also peer reviewed by journal JSS.


References in zbMATH (referenced in 103 articles , 1 standard article )

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  1. Golovkine, Steven; Klutchnikoff, Nicolas; Patilea, Valentin: Learning the smoothness of noisy curves with application to online curve estimation (2022)
  2. Matias D. Cattaneo, Michael Jansson, Xinwei Ma: lpdensity: Local Polynomial Density Estimation and Inference (2022) not zbMATH
  3. Han, Eric; Mojirsheibani, Majid: On histogram-based regression and classification with incomplete data (2021)
  4. Kim, Junsik; Loh, Ji Meng; Jang, Woncheol: Generalized bagging (2021)
  5. Nakarmi, Janet; Sang, Hailin; Ge, Lin: Variable bandwidth kernel regression estimation (2021)
  6. Norets, Andriy: Optimal auxiliary priors and reversible jump proposals for a class of variable dimension models (2021)
  7. Zhu, Xujia; Sudret, Bruno: Emulation of stochastic simulators using generalized lambda models (2021)
  8. Al-Sharadqah, Ali; Mojirsheibani, Majid: A simple approach to construct confidence bands for a regression function with incomplete data (2020)
  9. Chu, Ba M.; Jacho-Chávez, David T.; Linton, Oliver B.: Standard errors for nonparametric regression (2020)
  10. Cui, Zhenyu; Kirkby, Justin Lars; Nguyen, Duy: Nonparametric density estimation by B-spline duality (2020)
  11. Dabo-Niang, Sophie; Thiam, Baba: Kernel regression estimation with errors-in-variables for random fields (2020)
  12. Ferrara, Giancarlo: Stochastic frontier models using R (2020)
  13. Hušková, Marie; Meintanis, Simos G.; Pretorius, Charl: Tests for validity of the semiparametric heteroskedastic transformation model (2020)
  14. Kuchibhotla, Arun K.; Patra, Rohit K.: Efficient estimation in single index models through smoothing splines (2020)
  15. Loubes, Jean-Michel; Marteau, Clément; Solís, Maikol: Rates of convergence in conditional covariance matrix with nonparametric entries estimation (2020)
  16. Maria Xose Rodriguez-Alvarez, Vanda Inacio: ROCnReg: An R Package for Receiver Operating Characteristic Curve Inference with and without Covariate Information (2020) arXiv
  17. McCloud, Nadine; Parmeter, Christopher F.: Determining the number of effective parameters in kernel density estimation (2020)
  18. Nicolussi, Federica; Zoia, Maria Grazia: Gram-Charlier-like expansions of the convoluted hyperbolic-secant density (2020)
  19. Otneim, Håkon; Jullum, Martin; Tjøstheim, Dag: Pairwise local Fisher and naive Bayes: improving two standard discriminants (2020)
  20. Xie, Fangzheng; Xu, Yanxun: Adaptive Bayesian nonparametric regression using a kernel mixture of polynomials with application to partial linear models (2020)

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