alr3

alr3: Data to accompany Applied Linear Regression 3rd edition , This package is a companion to the textbook S. Weisberg (2005), ”Applied Linear Regression,” 3rd edition, Wiley. It includes all the data sets discussed in the book (except one), and a few functions that are tailored to the methods discussed in the book. As of version 2.0.0, this package depends on the car package. Many functions formerly in alr3 have been renamed and now reside in car. Data files have beeen lightly modified to make some data columns row labels. (Source: http://cran.r-project.org/web/packages)


References in zbMATH (referenced in 259 articles , 1 standard article )

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  1. Buccini, Alessandro; De la Cruz Cabrera, Omar; Donatelli, Marco; Martinelli, Andrea; Reichel, Lothar: Large-scale regression with non-convex loss and penalty (2020)
  2. Glaws, Andrew; Constantine, Paul G.; Cook, R. Dennis: Inverse regression for ridge recovery: a data-driven approach for parameter reduction in computer experiments (2020)
  3. Khachay, Michael; Neznakhina, Katherine: Complexity and approximability of the Euclidean generalized traveling salesman problem in grid clusters (2020)
  4. Bollhöfer, Matthias; Eftekhari, Aryan; Scheidegger, Simon; Schenk, Olaf: Large-scale sparse inverse covariance matrix estimation (2019)
  5. Cheng, Gang; Chen, Yen-Chi: Nonparametric inference via bootstrapping the debiased estimator (2019)
  6. Fang, Kuangnan; Fan, Xinyan; Lan, Wei; Wang, Bingquan: Nonparametric additive beta regression for fractional response with application to body fat data (2019)
  7. Gong, Zhaohua; Liu, Chongyang; Sun, Jie; Teo, Kok Lay: Distributionally robust (L_1)-estimation in multiple linear regression (2019)
  8. Negarestani, Hossein; Jamalizadeh, Ahad; Shafiei, Sobhan; Balakrishnan, Narayanaswamy: Mean mixtures of normal distributions: properties, inference and application (2019)
  9. Tsamardinos, Ioannis; Borboudakis, Giorgos; Katsogridakis, Pavlos; Pratikakis, Polyvios; Christophides, Vassilis: A greedy feature selection algorithm for big data of high dimensionality (2019)
  10. Chantarangsi, W.; Liu, W.; Bretz, F.; Kiatsupaibul, S.; Hayter, A. J.: Normal probability plots with confidence for the residuals in linear regression (2018)
  11. Charitidou, E.; Fouskakis, D.; Ntzoufras, I.: Objective Bayesian transformation and variable selection using default Bayes factors (2018)
  12. Cordeiro, Gauss M.; Yousof, Haitham M.; Ramires, Thiago G.; Ortega, Edwin M. M.: The Burr XII system of densities: properties, regression model and applications (2018)
  13. Eck, Daniel J.: Bootstrapping for multivariate linear regression models (2018)
  14. El Karoui, Noureddine; Purdom, Elizabeth: Can we trust the bootstrap in high-dimensions? The case of linear models (2018)
  15. Hokanson, Jeffrey M.; Constantine, Paul G.: Data-driven polynomial ridge approximation using variable projection (2018)
  16. Jamal, Farrukh; Aljarrah, Mohammad A.; Tahir, M. H.; Nasir, M. Arslan: A new extended generalized Burr-III family of distributions (2018)
  17. Lipponen, A.; Huttunen, J. M. J.; Romakkaniemi, S.; Kokkola, H.; Kolehmainen, V.: Correction of model reduction errors in simulations (2018)
  18. Liu, Xuqing; Gao, Feng; Wu, Yandong; Zhao, Zhiguo: Detecting outliers and influential points: an indirect classical Mahalanobis distance-based method (2018)
  19. Peng, Xinjun; Chen, De: PTSVRs: regression models via projection twin support vector machine (2018)
  20. Reid, Stephen; Taylor, Jonathan; Tibshirani, Robert: A general framework for estimation and inference from clusters of features (2018)

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