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:

References in zbMATH (referenced in 276 articles , 1 standard article )

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  1. Borboudakis, Giorgos; Tsamardinos, Ioannis: Extending greedy feature selection algorithms to multiple solutions (2021)
  2. Foti, Francesco; Geuzaine, Margaux; Denoël, Vincent: On the identification of the axial force and bending stiffness of stay cables anchored to flexible supports (2021)
  3. Freitas, Rodolfo S. M.; Barbosa, Carlos H. S.; Guerra, Gabriel M.; Coutinho, Alvaro L. G. A.; Rochinha, Fernando A.: An encoder-decoder deep surrogate for reverse time migration in seismic imaging under uncertainty (2021)
  4. Gómez, Andrés; Prokopyev, Oleg A.: A mixed-integer fractional optimization approach to best subset selection (2021)
  5. Mykland, Per A.; Zhang, Lan: The observed asymptotic variance: hard edges, and a regression approach (2021)
  6. Buccini, Alessandro; De la Cruz Cabrera, Omar; Donatelli, Marco; Martinelli, Andrea; Reichel, Lothar: Large-scale regression with non-convex loss and penalty (2020)
  7. Chen, Xiuping; Cai, Guanghui; Gao, Yan; Zhao, Shangwei: Asymptotic optimality of the nonnegative garrote estimator under heteroscedastic errors (2020)
  8. Glaws, Andrew; Constantine, Paul G.; Cook, R. Dennis: Inverse regression for ridge recovery: a data-driven approach for parameter reduction in computer experiments (2020)
  9. Khachay, Michael; Neznakhina, Katherine: Complexity and approximability of the Euclidean generalized traveling salesman problem in grid clusters (2020)
  10. Yang, Yang; Xiong, Ping; Huang, Qing; Chen, Fei: Secure and efficient outsourcing computation on large-scale linear regressions (2020)
  11. Bollhöfer, Matthias; Eftekhari, Aryan; Scheidegger, Simon; Schenk, Olaf: Large-scale sparse inverse covariance matrix estimation (2019)
  12. Cheng, Gang; Chen, Yen-Chi: Nonparametric inference via bootstrapping the debiased estimator (2019)
  13. Fang, Kuangnan; Fan, Xinyan; Lan, Wei; Wang, Bingquan: Nonparametric additive beta regression for fractional response with application to body fat data (2019)
  14. Gong, Zhaohua; Liu, Chongyang; Sun, Jie; Teo, Kok Lay: Distributionally robust (L_1)-estimation in multiple linear regression (2019)
  15. Negarestani, Hossein; Jamalizadeh, Ahad; Shafiei, Sobhan; Balakrishnan, Narayanaswamy: Mean mixtures of normal distributions: properties, inference and application (2019)
  16. Tsamardinos, Ioannis; Borboudakis, Giorgos; Katsogridakis, Pavlos; Pratikakis, Polyvios; Christophides, Vassilis: A greedy feature selection algorithm for big data of high dimensionality (2019)
  17. Chantarangsi, W.; Liu, W.; Bretz, F.; Kiatsupaibul, S.; Hayter, A. J.: Normal probability plots with confidence for the residuals in linear regression (2018)
  18. Charitidou, E.; Fouskakis, D.; Ntzoufras, I.: Objective Bayesian transformation and variable selection using default Bayes factors (2018)
  19. 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)
  20. Doğru, Fatma Zehra; Bulut, Y. Murat; Arslan, Olcay: Doubly reweighted estimators for the parameters of the multivariate t-distribution (2018)

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