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Citations for "Addendum: Regularization and variable selection via the elastic net"

by Hui Zou & Trevor Hastie

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  1. Lee, Youngjo & Oh, Hee-Seok, 2014. "A new sparse variable selection via random-effect model," Journal of Multivariate Analysis, Elsevier, Elsevier, vol. 125(C), pages 89-99.
  2. Jacobs, Jan P.A.M. & Otter, Pieter W. & den Reijer, Ard H.J., 2012. "Information, data dimension and factor structure," Journal of Multivariate Analysis, Elsevier, Elsevier, vol. 106(C), pages 80-91.
  3. Michael Schomaker, 2012. "Shrinkage averaging estimation," Statistical Papers, Springer, Springer, vol. 53(4), pages 1015-1034, November.
  4. Lykou, Anastasia & Whittaker, Joe, 2010. "Sparse CCA using a Lasso with positivity constraints," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 54(12), pages 3144-3157, December.
  5. Blommaert, A. & Hens, N. & Beutels, Ph., 2014. "Data mining for longitudinal data under multicollinearity and time dependence using penalized generalized estimating equations," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 71(C), pages 667-680.
  6. Eickmeier, Sandra & Ng, Tim, 2011. "Forecasting national activity using lots of international predictors: An application to New Zealand," International Journal of Forecasting, Elsevier, Elsevier, vol. 27(2), pages 496-511, April.
  7. Schumacher, Christian, 2010. "Factor forecasting using international targeted predictors: The case of German GDP," Economics Letters, Elsevier, Elsevier, vol. 107(2), pages 95-98, May.
  8. Yao, Weixin & Wang, Qin, 2013. "Robust variable selection through MAVE," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 63(C), pages 42-49.
  9. Tian, Tian Siva & James, Gareth M., 2013. "Interpretable dimension reduction for classifying functional data," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 57(1), pages 282-296.
  10. Matthias Weber & Martin Schumacher & and Harald Binder, 2014. "Regularized Regression Incorporating Network Information: Simultaneous Estimation of Covariate Coefficients and Connection Signs," Tinbergen Institute Discussion Papers 14-089/I, Tinbergen Institute.
  11. Shi, Minghui & Dunson, David B., 2011. "Bayesian variable selection via particle stochastic search," Statistics & Probability Letters, Elsevier, Elsevier, vol. 81(2), pages 283-291, February.
  12. Nott, David J. & Leng, Chenlei, 2010. "Bayesian projection approaches to variable selection in generalized linear models," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 54(12), pages 3227-3241, December.
  13. Mehmet Caner & Anders Bredahl Kock, 2013. "Oracle Inequalities for Convex Loss Functions with Non-Linear Targets," CREATES Research Papers 2013-51, School of Economics and Management, University of Aarhus.
  14. Alec Smith & B. Douglas Bernheim & Colin Camerer & Antonio Rangel, 2013. "Neural Activity Reveals Preferences Without Choices," NBER Working Papers 19270, National Bureau of Economic Research, Inc.
  15. Bessec, Marie, 2013. "Short-term forecasts of French GDP: A dynamic factor model with targeted predictors," Economics Papers from University Paris Dauphine, Paris Dauphine University 123456789/10079, Paris Dauphine University.
  16. Dimitris Korobilis, 2011. "Hierarchical Shrinkage Priors for Dynamic Regressions with Many Predictors," Working Paper Series, The Rimini Centre for Economic Analysis 21_11, The Rimini Centre for Economic Analysis.
  17. Jiang, Liewen & Bondell, Howard D. & Wang, Huixia Judy, 2014. "Interquantile shrinkage and variable selection in quantile regression," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 69(C), pages 208-219.
  18. Friedman, Jerome H., 2012. "Fast sparse regression and classification," International Journal of Forecasting, Elsevier, Elsevier, vol. 28(3), pages 722-738.
  19. Korzeń, M. & Jaroszewicz, S. & Klęsk, P., 2013. "Logistic regression with weight grouping priors," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 64(C), pages 281-298.
  20. Fan, Jianqing & Liao, Yuan, 2012. "Endogeneity in ultrahigh dimension," MPRA Paper 38698, University Library of Munich, Germany.
  21. McKay Curtis, S. & Banerjee, Sayantan & Ghosal, Subhashis, 2014. "Fast Bayesian model assessment for nonparametric additive regression," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 71(C), pages 347-358.
  22. Jiahan Li & Ilias Tsiakas & Wei Wang, 2014. "Predicting Exchange Rates Out of Sample: Can Economic Fundamentals Beat the Random Walk?," Working Paper Series, The Rimini Centre for Economic Analysis 05_14, The Rimini Centre for Economic Analysis.
  23. Wang, Xiaoming & Park, Taesung & Carriere, K.C., 2010. "Variable selection via combined penalization for high-dimensional data analysis," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 54(10), pages 2230-2243, October.
  24. Shen, Haipeng & Huang, Jianhua Z., 2008. "Sparse principal component analysis via regularized low rank matrix approximation," Journal of Multivariate Analysis, Elsevier, Elsevier, vol. 99(6), pages 1015-1034, July.
  25. Wolfgang Karl Härdle & Dedy Dwi Prastyo, 2013. "Default Risk Calculation based on Predictor Selection for the Southeast Asian Industry," SFB 649 Discussion Papers SFB649DP2013-037, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  26. Lian, Heng, 2012. "Shrinkage estimation for identification of linear components in additive models," Statistics & Probability Letters, Elsevier, Elsevier, vol. 82(2), pages 225-231.
  27. Kim, Hyun Hak & Swanson, Norman R., 2014. "Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence," Journal of Econometrics, Elsevier, Elsevier, vol. 178(P2), pages 352-367.
  28. Ueki, Masao & Kawasaki, Yoshinori, 2013. "Multiple choice from competing regression models under multicollinearity based on standardized update," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 63(C), pages 31-41.
  29. Wang, Qin & Yin, Xiangrong, 2008. "A nonlinear multi-dimensional variable selection method for high dimensional data: Sparse MAVE," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 52(9), pages 4512-4520, May.
  30. Mielniczuk, Jan & Teisseyre, Paweł, 2014. "Using random subspace method for prediction and variable importance assessment in linear regression," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 71(C), pages 725-742.
  31. Feng Li & Lu Lin & Yuxia Su, 2013. "Variable selection and parameter estimation for partially linear models via Dantzig selector," Metrika, Springer, Springer, vol. 76(2), pages 225-238, February.
  32. Ard Reijer, 2013. "Forecasting Dutch GDP and inflation using alternative factor model specifications based on large and small datasets," Empirical Economics, Springer, Springer, vol. 44(2), pages 435-453, April.
  33. Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2013. "Complete subset regressions," Journal of Econometrics, Elsevier, Elsevier, vol. 177(2), pages 357-373.
  34. Ruggieri, Eric & Lawrence, Charles E., 2012. "On efficient calculations for Bayesian variable selection," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 56(6), pages 1319-1332.
  35. K. Kampa & S. Mehta & C. Chou & W. Chaovalitwongse & T. Grabowski, 2014. "Sparse optimization in feature selection: application in neuroimaging," Journal of Global Optimization, Springer, Springer, vol. 59(2), pages 439-457, July.
  36. Lin, Huazhen & Peng, Heng, 2013. "Smoothed rank correlation of the linear transformation regression model," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 57(1), pages 615-630.
  37. van Wieringen, Wessel N. & Kun, David & Hampel, Regina & Boulesteix, Anne-Laure, 2009. "Survival prediction using gene expression data: A review and comparison," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 53(5), pages 1590-1603, March.
  38. Peter Bühlmann & Jacopo Mandozzi, 2014. "High-dimensional variable screening and bias in subsequent inference, with an empirical comparison," Computational Statistics, Springer, Springer, vol. 29(3), pages 407-430, June.
  39. Huang, Zhensheng & Pang, Zhen & Lin, Bingqing & Shao, Quanxi, 2014. "Model structure selection in single-index-coefficient regression models," Journal of Multivariate Analysis, Elsevier, Elsevier, vol. 125(C), pages 159-175.
  40. Julius Stakenas, 2012. "Generating short-term forecasts of the Lithuanian GDP using factor models," Bank of Lithuania Working Paper Series, Bank of Lithuania 13, Bank of Lithuania.
  41. Stefan Kerbl & Michael Sigmund, 2011. "What Drives Aggregate Credit Risk?," Financial Stability Report, Oesterreichische Nationalbank (Austrian Central Bank), Oesterreichische Nationalbank (Austrian Central Bank), issue 22, pages 72-87.
  42. Wang, Tao & Zhu, Lixing, 2011. "Consistent tuning parameter selection in high dimensional sparse linear regression," Journal of Multivariate Analysis, Elsevier, Elsevier, vol. 102(7), pages 1141-1151, August.
  43. Hyun Hak Kim & Norman Swanson, 2013. "Mining Big Data Using Parsimonious Factor and Shrinkage Methods," Departmental Working Papers, Rutgers University, Department of Economics 201316, Rutgers University, Department of Economics.
  44. Charles Bouveyron & Camille Brunet-Saumard, 2014. "Discriminative variable selection for clustering with the sparse Fisher-EM algorithm," Computational Statistics, Springer, Springer, vol. 29(3), pages 489-513, June.
  45. Wang, Tao & Zhu, Lixing, 2013. "Sparse sufficient dimension reduction using optimal scoring," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 57(1), pages 223-232.
  46. Roberts, S. & Nowak, G., 2014. "Stabilizing the lasso against cross-validation variability," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 70(C), pages 198-211.
  47. Joscha Beckmann & Rainer Schüssler, 2014. "Forecasting Exchange Rates under Model and Parameter Uncertainty," CQE Working Papers, Center for Quantitative Economics (CQE), University of Muenster 3214, Center for Quantitative Economics (CQE), University of Muenster.
  48. Chalise, Prabhakar & Fridley, Brooke L., 2012. "Comparison of penalty functions for sparse canonical correlation analysis," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 56(2), pages 245-254.
  49. Shutes, Karl & Adcock, Chris, 2013. "Regularized Skew-Normal Regression," MPRA Paper 52217, University Library of Munich, Germany, revised 11 Dec 2013.
  50. Xia, X.H. & Huang, G.T. & Chen, G.Q. & Zhang, Bo & Chen, Z.M. & Yang, Q., 2011. "Energy security, efficiency and carbon emission of Chinese industry," Energy Policy, Elsevier, Elsevier, vol. 39(6), pages 3520-3528, June.
  51. Chakraborty, Sounak, 2009. "Bayesian binary kernel probit model for microarray based cancer classification and gene selection," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 53(12), pages 4198-4209, October.
  52. McCann, Lauren & Welsch, Roy E., 2007. "Robust variable selection using least angle regression and elemental set sampling," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 52(1), pages 249-257, September.
  53. Nott, David J., 2008. "Predictive performance of Dirichlet process shrinkage methods in linear regression," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 52(7), pages 3658-3669, March.
  54. Daye, Z. John & Jeng, X. Jessie, 2009. "Shrinkage and model selection with correlated variables via weighted fusion," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 53(4), pages 1284-1298, February.
  55. Changrong Yan & Dixin Zhang, 2013. "Sparse dimension reduction for survival data," Computational Statistics, Springer, Springer, vol. 28(4), pages 1835-1852, August.
  56. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, Elsevier, vol. 146(2), pages 304-317, October.
  57. Wang, Mingqiu & Song, Lixin & Wang, Xiaoguang, 2010. "Bridge estimation for generalized linear models with a diverging number of parameters," Statistics & Probability Letters, Elsevier, Elsevier, vol. 80(21-22), pages 1584-1596, November.
  58. Tutz, Gerhard & Binder, Harald, 2007. "Boosting ridge regression," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 51(12), pages 6044-6059, August.
  59. Gefang, Deborah, 2014. "Bayesian doubly adaptive elastic-net Lasso for VAR shrinkage," International Journal of Forecasting, Elsevier, Elsevier, vol. 30(1), pages 1-11.
  60. Chakraborty, Sounak & Guo, Ruixin, 2011. "A Bayesian hybrid Huberized support vector machine and its applications in high-dimensional medical data," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 55(3), pages 1342-1356, March.
  61. Baragatti, M. & Pommeret, D., 2012. "A study of variable selection using g-prior distribution with ridge parameter," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 56(6), pages 1920-1934.
  62. Zhou, Ding-Xuan, 2013. "On grouping effect of elastic net," Statistics & Probability Letters, Elsevier, Elsevier, vol. 83(9), pages 2108-2112.