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 227 articles , 1 standard article )

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  1. Bollhöfer, Matthias; Eftekhari, Aryan; Scheidegger, Simon; Schenk, Olaf: Large-scale sparse inverse covariance matrix estimation (2019)
  2. Tsamardinos, Ioannis; Borboudakis, Giorgos; Katsogridakis, Pavlos; Pratikakis, Polyvios; Christophides, Vassilis: A greedy feature selection algorithm for big data of high dimensionality (2019)
  3. Chantarangsi, W.; Liu, W.; Bretz, F.; Kiatsupaibul, S.; Hayter, A. J.: Normal probability plots with confidence for the residuals in linear regression (2018)
  4. Charitidou, E.; Fouskakis, D.; Ntzoufras, I.: Objective Bayesian transformation and variable selection using default Bayes factors (2018)
  5. Eck, Daniel J.: Bootstrapping for multivariate linear regression models (2018)
  6. El Karoui, Noureddine; Purdom, Elizabeth: Can we trust the bootstrap in high-dimensions? The case of linear models (2018)
  7. Hokanson, Jeffrey M.; Constantine, Paul G.: Data-driven polynomial ridge approximation using variable projection (2018)
  8. Jamal, Farrukh; Aljarrah, Mohammad A.; Tahir, M. H.; Nasir, M. Arslan: A new extended generalized Burr-III family of distributions (2018)
  9. Reid, Stephen; Taylor, Jonathan; Tibshirani, Robert: A general framework for estimation and inference from clusters of features (2018)
  10. Jha, Susmit; Seshia, Sanjit A.: A theory of formal synthesis via inductive learning (2017)
  11. Savchuk, Olga Y.; Hart, Jeffrey D.: Fully robust one-sided cross-validation for regression functions (2017)
  12. Vanneschi, Leonardo: An introduction to geometric semantic genetic programming (2017)
  13. Vujicic, Tijana; Glass, Jesse; Zhou, Fang; Obradovic, Zoran: Gaussian conditional random fields extended for directed graphs (2017)
  14. Wang, Tao; Wen, Xuerong Meggie; Zhu, Lixing: Multiple-population shrinkage estimation via sliced inverse regression (2017)
  15. Werner, Christoph; Bedford, Tim; Cooke, Roger M.; Hanea, Anca M.; Morales-Nápoles, Oswaldo: Expert judgement for dependence in probabilistic modelling: a systematic literature review and future research directions (2017)
  16. Babadi, B.; Rasekh, A.; Zare, K.; Rasekhi, A. A.: A variance shift model for detection of outliers in the linear mixed measurement error models (2016)
  17. Bertsimas, Dimitris; King, Angela: OR forum: An algorithmic approach to linear regression (2016)
  18. Faraway, Julian J.: Extending the linear model with R. Generalized linear, mixed effects and nonparametric regression models. (2016)
  19. Kim, Yongku; Berliner, L. Mark: Change of spatiotemporal scale in dynamic models (2016)
  20. Parrish, Joan; Crunk, Steven M.; Lee, Bee Leng: The Yule-Walker equations as a weighted least-squares problem and the association with tapering (2016)

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