lspartition-package: Nonparametric Estimation and Inference using...
In lspartition: Nonparametric Estimation and Inference Procedures using Partitioning-Based Least Squares Regression
Description
Author(s)
References
Description
This package provides tools for statistical analysis using B-splines, wavelets, and
piecewise polynomials as described in
Cattaneo, Farrell and Feng (2019a):
lsprobust for least squares point estimation with robust bias-corrected pointwise and
uniform inference procedures; lspkselect for data-driven procedures
for selecting the IMSE-optimal number of partitioning knots; lsprobust.plot
for regression plots with robust confidence intervals and confidence bands;
lsplincom for estimation and inference for linear combination of regression
functions of different groups.
The companion software article, Cattaneo, Farrell and Feng (2019b),
provides further implementation details and empirical illustrations.
Author(s)
Matias D. Cattaneo, Princeton University, Princeton, NJ. cattaneo@princeton.edu.
Max H. Farrell, University of Chicago, Chicago, IL. max.farrell@chicagobooth.edu.
Yingjie Feng (maintainer), Princeton University, Princeton, NJ. yingjief@princeton.edu.
References
Cattaneo, M. D., M. H. Farrell, and Y. Feng (2019a): Large Sample Properties of Partitioning-Based Series Estimators. Annals of Statistics, forthcoming. arXiv:1804.04916.
Cattaneo, M. D., M. H. Farrell, and Y. Feng (2019b): lspartition: Partitioning-Based Least Squares Regression. R Journal, forthcoming. arXiv:1906.00202.
lspartition documentation built on Aug. 9, 2019, 1:03 a.m.
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Description Author(s) References
Description
This package provides tools for statistical analysis using B-splines, wavelets, and
piecewise polynomials as described in
Cattaneo, Farrell and Feng (2019a):
lsprobust for least squares point estimation with robust bias-corrected pointwise and
uniform inference procedures; lspkselect for data-driven procedures
for selecting the IMSE-optimal number of partitioning knots; lsprobust.plot
for regression plots with robust confidence intervals and confidence bands;
lsplincom for estimation and inference for linear combination of regression
functions of different groups.
The companion software article, Cattaneo, Farrell and Feng (2019b), provides further implementation details and empirical illustrations.
Author(s)
Matias D. Cattaneo, Princeton University, Princeton, NJ. cattaneo@princeton.edu.
Max H. Farrell, University of Chicago, Chicago, IL. max.farrell@chicagobooth.edu.
Yingjie Feng (maintainer), Princeton University, Princeton, NJ. yingjief@princeton.edu.
References
Cattaneo, M. D., M. H. Farrell, and Y. Feng (2019a): Large Sample Properties of Partitioning-Based Series Estimators. Annals of Statistics, forthcoming. arXiv:1804.04916.
Cattaneo, M. D., M. H. Farrell, and Y. Feng (2019b): lspartition: Partitioning-Based Least Squares Regression. R Journal, forthcoming. arXiv:1906.00202.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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