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Appl. Stat. 1(2), 302–332 (2007)
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coordinate descent. J. Stat. Softw. 33(1), 1–22 (2010)
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sonar targets. Neural Netw. 1(1), 75–89 (1988)
13. R.B. Gramacy, monomvn: estimation for multivariate normal and Student-t data with
monotone missingness (2017). R package version 1.9-7
14. I. Hacking, The Emergence of Probability: A Philosophical Study of Early Ideas about
Probability, Induction and Statistical Inference (Cambridge University Press, Cambridge,
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Statistics (Springer, New York, 2001)
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Generalizations. Chapman & Hall/CRC Monographs on Statistics & Applied Probability
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shrinkage parameter. Stat. Comput. 23(3), 361–390 (2013)
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Softw. 42(9), 22 (2011)
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Publishing Company, New York, 2014)
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9(2), 319–337 (2000)
22. T. Park, G. Casella, The Bayesian Lasso. J. Am. Stat. Assoc. 103(482), 681–686 (2008)
23. V. Ro˘ cková, E.I. George, The spike-and-slab Lasso. J. Am. Stat. Assoc. 113(521), 431–444
(2018)
24. R. Tibshirani, Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. Series B Stat.
Methodol. 58(1), 267–288 (1996)
25. R. Tibshirani, Regression shrinkage and selection via the Lasso: a retrospective. J. R. Stat.
Soc. Series B Stat. Methodol. 73(3), 273–282 (2011)
26. M. Yuan, Y. Lin, On the non-negative garrotte estimator. J. R. Stat. Soc. Series B Stat.
Methodol. 69(2), 143–161 (2007)
109
8. ESA science & technology: Gaia. http://sci.esa.int/gaia. Accessed 6 Feb 2018
9. L. Fahrmeir, G. Tutz, W. Hennevogl, Multivariate Statistical Modelling Based on Generalized
Linear Models. Springer Series in Statistics (Springer, New York, 2001)
10. J. Friedman, T. Hastie, H. Hofling, R. Tibshirani, Pathwise coordinate optimization. Ann.
Appl. Stat. 1(2), 302–332 (2007)
11. J. Friedman, T. Hastie, R. Tibshirani, Regularization paths for generalized linear models via
coordinate descent. J. Stat. Softw. 33(1), 1–22 (2010)
12. R.P. Gorman, T.J. Sejnowski, Analysis of hidden units in a layered network trained to classify
sonar targets. Neural Netw. 1(1), 75–89 (1988)
13. R.B. Gramacy, monomvn: estimation for multivariate normal and Student-t data with
monotone missingness (2017). R package version 1.9-7
14. I. Hacking, The Emergence of Probability: A Philosophical Study of Early Ideas about
Probability, Induction and Statistical Inference (Cambridge University Press, Cambridge,
1975)
15. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning. Springer Series in
Statistics (Springer, New York, 2001)
16. T. Hastie, R. Tibshirani, M. Wainwright, Statistical Learning with Sparsity: The Lasso and
Generalizations. Chapman & Hall/CRC Monographs on Statistics & Applied Probability
(CRC Press, Boca Raton, 2015)
17. F. Leisch, E. Dimitriadou, mlbench: machine learning benchmark problems (2010). R package
version 2.1-1
18. A. Lykou, I. Ntzoufras, On Bayesian Lasso variable selection and the specification of the
shrinkage parameter. Stat. Comput. 23(3), 361–390 (2013)
19. A.D. Martin, K.M. Quinn, J.H. Park, MCMCpack: Markov chain Monte Carlo in R. J. Stat.
Softw. 42(9), 22 (2011)
20. Y. Nesterov, Introductory Lectures on Convex Optimization: A Basic Course, 1st edn. (Springer
Publishing Company, New York, 2014)
21. M.R. Osborne, B. Presnell, B.A. Turlach, On the Lasso and its dual. J. Comput. Graph. Stat.
9(2), 319–337 (2000)
22. T. Park, G. Casella, The Bayesian Lasso. J. Am. Stat. Assoc. 103(482), 681–686 (2008)
23. V. Ro˘ cková, E.I. George, The spike-and-slab Lasso. J. Am. Stat. Assoc. 113(521), 431–444
(2018)
24. R. Tibshirani, Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. Series B Stat.
Methodol. 58(1), 267–288 (1996)
25. R. Tibshirani, Regression shrinkage and selection via the Lasso: a retrospective. J. R. Stat.
Soc. Series B Stat. Methodol. 73(3), 273–282 (2011)
26. M. Yuan, Y. Lin, On the non-negative garrotte estimator. J. R. Stat. Soc. Series B Stat.
Methodol. 69(2), 143–161 (2007)