ARC

Applied regression including computing and graphics. This text is for readers who want to learn applied regression without loading heavy mathematical burden. For such a target group it is excellent. With emphasis on exploratory tools it starts with simple linear regression, then deals with multiple regression and goes to logistic regression and generalized linear models. It has discussions of response and predictor transformations, diagnostics, and regression graphics. Graphical methods are offered as the key to understanding the methods. In some sense the book can be seen as an introduction to and a guided tour through ARC. This is a free software package which can be downloaded from the web. All the graphics presented in the book can be performed with it.


References in zbMATH (referenced in 39 articles )

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  1. Atkinson, Anthony C.; Riani, Marco; Corbellini, Aldo: The Box-Cox transformation: review and extensions (2021)
  2. Li, Xiangjie; Zhang, Jingxiao: Sufficient dimension folding via tensor inverse regression (2020)
  3. Šibalić, N.; Pritchard, J. D.; Adams, C. S.; Weatherill, K. J.: ARC: an open-source library for calculating properties of alkali Rydberg atoms (2017)
  4. Lizotte, Daniel J.; Laber, Eric B.: Multi-objective Markov decision processes for data-driven decision support (2016)
  5. Shi, Lei; Lu, Jun; Zhao, Jianhua; Chen, Gemai: Case deletion diagnostics for GMM estimation (2016)
  6. Zhou, Jingke; Zhu, Lixing: Principal minimax support vector machine for sufficient dimension reduction with contaminated data (2016)
  7. Liu, Xuejing; Yu, Zhou; Wen, Xuerong Meggie; Paige, Robert: On testing common indices for two multi-index models: a link-free approach (2015)
  8. Haggag, Magda M. M.: Combining of dimension reduction regression methods (2014)
  9. Víšek, Jan Ámos: Diagnostics of robust identification of model (2014)
  10. Bercu, Bernard; Nguyen, Thi Mong Ngoc; Saracco, Jérôme: A new approach on recursive and non-recursive SIR methods (2012)
  11. Liquet, Benoît; Saracco, Jérôme: A graphical tool for selecting the number of slices and the dimension of the model in SIR and SAVE approaches (2012)
  12. Qiu, Peihua; Li, Zhonghua: Distribution-free monitoring of univariate processes (2011)
  13. Atkinson, Anthony C.; Riani, Marco; Cerioli, Andrea: Rejoinder: The forward search: theory and data analysis (2010)
  14. Dal Bello, L. H. A.; Vieira, A. F. C.: Optimization of a product performance using mixture experiments (2010)
  15. Kuentz, Vanessa; Saracco, Jérôme: Cluster-based sliced inverse regression (2010)
  16. Wheeler, David C.; Hickson, Demarc A.; Waller, Lance A.: Assessing local model adequacy in Bayesian hierarchical models using the partitioned deviance information criterion (2010)
  17. Zhang, Yongli; Shen, Xiaotong: Model selection procedure for high-dimensional data (2010)
  18. Nelson, David; Noorbaloochi, Siamak: Dimension reduction summaries for balanced contrasts (2009)
  19. Qiu, Peihua; Yang, Rong; Potegal, Michael: Statistical modeling of the time course of tantrum anger (2009)
  20. Shao, Yongwu; Cook, R. Dennis; Weisberg, Sanford: Partial central subspace and sliced average variance estimation (2009)

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