liureg: A Comprehensive R Package for the Liu Estimation of Linear Regression Model with Collinear Regressors

The Liu regression estimator is now a commonly used alternative to the conventional ordinary least squares estimator that avoids the adverse effects in the situations when there exists a considerable degree of multicollinearity among the regressors. There are only a few software packages available for estimation of the Liu regression coefficients, though with limited methods to estimate the Liu biasing parameter without addressing testing procedures. Our liureg package can be used to estimate the Liu regression coefficients utilizing a range of different existing biasing parameters, to test these coefficients with more than 15 Liu related statistics, and to present different graphical displays of these statistics.

Muhammad Imdadullah , Muhammad Aslam , Saima Altaf
2017-10-24

Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at RJ-2017-048.zip

CRAN packages used

lrmest, ltsbase, liureg, lmridge, MASS, mctest

CRAN Task Views implied by cited packages

Distributions, Econometrics, Environmetrics, Multivariate, NumericalMathematics, Psychometrics, Robust, SocialSciences

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

Citation

For attribution, please cite this work as

Imdadullah, et al., "liureg: A Comprehensive R Package for the Liu Estimation of Linear Regression Model with Collinear Regressors", The R Journal, 2017

BibTeX citation

@article{RJ-2017-048,
  author = {Imdadullah, Muhammad and Aslam, Muhammad and Altaf, Saima},
  title = {liureg: A Comprehensive R Package for the Liu Estimation of Linear Regression Model with Collinear Regressors},
  journal = {The R Journal},
  year = {2017},
  note = {https://doi.org/10.32614/RJ-2017-048},
  doi = {10.32614/RJ-2017-048},
  volume = {9},
  issue = {2},
  issn = {2073-4859},
  pages = {232-247}
}