care

R package care. High-Dimensional Regression and CAR Score Variable Selection. Implements the regression approach of Zuber and Strimmer (2011) ”High-dimensional regression and variable selection using CAR scores” SAGMB 10: 34, <<a href=”http://dx.doi.org/10.2202/1544-6115.1730”>doi:10.2202/1544-6115.1730</a>>. CAR scores measure the correlation between the response and the Mahalanobis-decorrelated predictors. The squared CAR score is a natural measure of variable importance and provides a canonical ordering of variables. This package provides functions for estimating CAR scores, for variable selection using CAR scores, and for estimating corresponding regression coefficients. Both shrinkage as well as empirical estimators are available.

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References in zbMATH (referenced in 7 articles , 1 standard article )

Showing results 1 to 7 of 7.
Sorted by year (citations)

  1. de Carvalho Barreto, Ikaro Daniel; Dore, Luiz Henrique; Stosic, Tatijana; Stosic, Borko D.: Extending DFA-based multiple linear regression inference: application to acoustic impedance models (2021)
  2. Crager, Michael R.: Extensions of the absolute standardized hazard ratio and connections with measures of explained variation and variable importance (2020)
  3. Furmańczyk, Konrad; Rejchel, Wojciech: High-dimensional linear model selection motivated by multiple testing (2020)
  4. Posch, Konstantin; Arbeiter, Maximilian; Pilz, Juergen: A novel Bayesian approach for variable selection in linear regression models (2020)
  5. Teisseyre, Paweł; Kłopotek, Robert A.; Mielniczuk, Jan: Random subspace method for high-dimensional regression with the \textttRpackage \textttregRSM (2016)
  6. Wang, Chamont; Gevertz, Jana L.: Finding causative genes from high-dimensional data: an appraisal of statistical and machine learning approaches (2016)
  7. Zuber, Verena; Strimmer, Korbinian: High-dimensional regression and variable selection using CAR scores (2011)


Further publications can be found at: http://strimmerlab.org/software/care/