IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login

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

by Hui Zou & Trevor Hastie

For a complete description of this item, click here. For a RSS feed for citations of this item, click here.
as in new window

  1. Wang, Qin & Yin, Xiangrong, 2008. "A nonlinear multi-dimensional variable selection method for high dimensional data: Sparse MAVE," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4512-4520, May.
  2. Roberto Casarin & Daniel Felix Ahelegbey & Monica Billio, 2014. "Sparse Graphical Vector Autoregression: A Bayesian Approach," Working Papers 2014:29, Department of Economics, University of Venice "Ca' Foscari".
  3. Julius Stakenas, 2012. "Generating short-term forecasts of the Lithuanian GDP using factor models," Bank of Lithuania Working Paper Series 13, Bank of Lithuania.
  4. Ueki, Masao & Kawasaki, Yoshinori, 2013. "Multiple choice from competing regression models under multicollinearity based on standardized update," Computational Statistics & Data Analysis, Elsevier, vol. 63(C), pages 31-41.
  5. Nott, David J., 2008. "Predictive performance of Dirichlet process shrinkage methods in linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3658-3669, March.
  6. K. Kampa & S. Mehta & C. Chou & W. Chaovalitwongse & T. Grabowski, 2014. "Sparse optimization in feature selection: application in neuroimaging," Journal of Global Optimization, Springer, vol. 59(2), pages 439-457, July.
  7. Jiahan Li & Ilias Tsiakas & Wei Wang, 2014. "Predicting Exchange Rates Out of Sample: Can Economic Fundamentals Beat the Random Walk?," Working Paper Series 05_14, The Rimini Centre for Economic Analysis.
  8. Xing, Xin & Hu, Jinjin & Yang, Yaning, 2014. "Robust minimum variance portfolio with L-infinity constraints," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 107-117.
  9. Rohart, Florian & San Cristobal, Magali & Laurent, Béatrice, 2014. "Selection of fixed effects in high dimensional linear mixed models using a multicycle ECM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 209-222.
  10. Chakraborty, Sounak, 2009. "Bayesian binary kernel probit model for microarray based cancer classification and gene selection," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4198-4209, October.
  11. 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, vol. 53(5), pages 1590-1603, March.
  12. Shi, Minghui & Dunson, David B., 2011. "Bayesian variable selection via particle stochastic search," Statistics & Probability Letters, Elsevier, vol. 81(2), pages 283-291, February.
  13. Kim, Hyun Hak & Swanson, Norman R., 2014. "Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence," Journal of Econometrics, Elsevier, vol. 178(P2), pages 352-367.
  14. Matthias Weber & Martin Schumacher & 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.
  15. Sandra Eickmeier & Tim Ng, 2009. "Forecasting national activity using lots of international predictors: an application to New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2009/04, Reserve Bank of New Zealand.
  16. Korobilis, Dimitris, 2013. "Hierarchical shrinkage priors for dynamic regressions with many predictors," International Journal of Forecasting, Elsevier, vol. 29(1), pages 43-59.
  17. Lian, Heng, 2012. "Shrinkage estimation for identification of linear components in additive models," Statistics & Probability Letters, Elsevier, vol. 82(2), pages 225-231.
  18. Jan Jacobs & Pieter Otter & Ard den Reijer, 2007. "Information, data dimension and factor structure," DNB Working Papers 150, Netherlands Central Bank, Research Department.
  19. Ard Reijer, 2013. "Forecasting Dutch GDP and inflation using alternative factor model specifications based on large and small datasets," Empirical Economics, Springer, vol. 44(2), pages 435-453, April.
  20. Shutes, Karl & Adcock, Chris, 2013. "Regularized Skew-Normal Regression," MPRA Paper 52217, University Library of Munich, Germany, revised 11 Dec 2013.
  21. Shuichi Kawano, 2014. "Selection of tuning parameters in bridge regression models via Bayesian information criterion," Statistical Papers, Springer, vol. 55(4), pages 1207-1223, November.
  22. Stefan Kerbl & Michael Sigmund, 2011. "What Drives Aggregate Credit Risk?," Financial Stability Report, Oesterreichische Nationalbank (Austrian Central Bank), issue 22, pages 72-87.
  23. Jiang, Liewen & Bondell, Howard D. & Wang, Huixia Judy, 2014. "Interquantile shrinkage and variable selection in quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 208-219.
  24. 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.
  25. Korzeń, M. & Jaroszewicz, S. & Klęsk, P., 2013. "Logistic regression with weight grouping priors," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 281-298.
  26. Marie Bessec, 2013. "Short‐Term Forecasts of French GDP: A Dynamic Factor Model with Targeted Predictors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(6), pages 500-511, 09.
  27. Baragatti, M. & Pommeret, D., 2012. "A study of variable selection using g-prior distribution with ridge parameter," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1920-1934.
  28. Charles Bouveyron & Camille Brunet-Saumard, 2014. "Discriminative variable selection for clustering with the sparse Fisher-EM algorithm," Computational Statistics, Springer, vol. 29(3), pages 489-513, June.
  29. Plakandaras, Vasilios & Gupta, Rangan & Gogas, Periklis & Papadimitriou, Theophilos, 2015. "Forecasting the U.S. real house price index," Economic Modelling, Elsevier, vol. 45(C), pages 259-267.
  30. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
  31. Schumacher, Christian, 2010. "Factor forecasting using international targeted predictors: The case of German GDP," Economics Letters, Elsevier, vol. 107(2), pages 95-98, May.
  32. Daye, Z. John & Jeng, X. Jessie, 2009. "Shrinkage and model selection with correlated variables via weighted fusion," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1284-1298, February.
  33. Ruggieri, Eric & Lawrence, Charles E., 2012. "On efficient calculations for Bayesian variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1319-1332.
  34. Lin, Huazhen & Peng, Heng, 2013. "Smoothed rank correlation of the linear transformation regression model," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 615-630.
  35. Gerda Claeskens, 2012. "Focused estimation and model averaging with penalization methods: an overview," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(3), pages 272-287, 08.
  36. Wang, Tao & Zhu, Lixing, 2013. "Sparse sufficient dimension reduction using optimal scoring," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 223-232.
  37. Mielniczuk, Jan & Teisseyre, Paweł, 2014. "Using random subspace method for prediction and variable importance assessment in linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 725-742.
  38. Matthias Weber & Martin Schumacher & 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.
  39. 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.
  40. Chalise, Prabhakar & Fridley, Brooke L., 2012. "Comparison of penalty functions for sparse canonical correlation analysis," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 245-254.
  41. Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2013. "Complete subset regressions," Journal of Econometrics, Elsevier, vol. 177(2), pages 357-373.
  42. Fan, Jianqing & Liao, Yuan, 2012. "Endogeneity in ultrahigh dimension," MPRA Paper 38698, University Library of Munich, Germany.
  43. Peter Bühlmann, 2013. "Causal statistical inference in high dimensions," Mathematical Methods of Operations Research, Springer, vol. 77(3), pages 357-370, June.
  44. Florian Ziel, 2015. "Iteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to AR-ARCH type processes," Papers 1502.06557, arXiv.org.
  45. Malefaki, Valia, 2015. "On Flexible Linear Factor Stochastic Volatility Models," MPRA Paper 62216, University Library of Munich, Germany.
  46. Marcelo C. Medeiros & Eduardo F. Mendes, 2015. "l1-Regularization of High-Dimensional Time-Series Models with Flexible Innovations," Textos para discussão 636, Department of Economics PUC-Rio (Brazil).
  47. Michael Schomaker, 2012. "Shrinkage averaging estimation," Statistical Papers, Springer, vol. 53(4), pages 1015-1034, November.
  48. Tutz, Gerhard & Binder, Harald, 2007. "Boosting ridge regression," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6044-6059, August.
  49. Lee, Youngjo & Oh, Hee-Seok, 2014. "A new sparse variable selection via random-effect model," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 89-99.
  50. Tian, Tian Siva & James, Gareth M., 2013. "Interpretable dimension reduction for classifying functional data," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 282-296.
  51. Joscha Beckmann & Rainer Schüssler, 2014. "Forecasting Exchange Rates under Model and Parameter Uncertainty," CQE Working Papers 3214, Center for Quantitative Economics (CQE), University of Muenster.
  52. Nott, David J. & Leng, Chenlei, 2010. "Bayesian projection approaches to variable selection in generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3227-3241, December.
  53. 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, vol. 39(6), pages 3520-3528, June.
  54. Gefang, Deborah, 2014. "Bayesian doubly adaptive elastic-net Lasso for VAR shrinkage," International Journal of Forecasting, Elsevier, vol. 30(1), pages 1-11.
  55. Mai, Qing & Zou, Hui, 2015. "Sparse semiparametric discriminant analysis," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 175-188.
  56. Daeju Kim & Shuichi Kawano & Yoshiyuki Ninomiya, 2014. "Adaptive basis expansion via $$\ell _1$$ ℓ 1 trend filtering," Computational Statistics, Springer, vol. 29(5), pages 1005-1023, October.
  57. Li, Jiahan & Chen, Weiye, 2014. "Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models," International Journal of Forecasting, Elsevier, vol. 30(4), pages 996-1015.
  58. Peter Bühlmann & Jacopo Mandozzi, 2014. "High-dimensional variable screening and bias in subsequent inference, with an empirical comparison," Computational Statistics, Springer, vol. 29(3), pages 407-430, June.
  59. McKay Curtis, S. & Banerjee, Sayantan & Ghosal, Subhashis, 2014. "Fast Bayesian model assessment for nonparametric additive regression," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 347-358.
  60. McCann, Lauren & Welsch, Roy E., 2007. "Robust variable selection using least angle regression and elemental set sampling," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 249-257, September.
  61. Shutes, Karl & Adcock, Chris, 2013. "Regularized Extended Skew-Normal Regression," MPRA Paper 58445, University Library of Munich, Germany, revised 09 Sep 2014.
  62. Feng Li & Lu Lin & Yuxia Su, 2013. "Variable selection and parameter estimation for partially linear models via Dantzig selector," Metrika, Springer, vol. 76(2), pages 225-238, February.
  63. Friedman, Jerome H., 2012. "Fast sparse regression and classification," International Journal of Forecasting, Elsevier, vol. 28(3), pages 722-738.
  64. Changrong Yan & Dixin Zhang, 2013. "Sparse dimension reduction for survival data," Computational Statistics, Springer, vol. 28(4), pages 1835-1852, August.
  65. Roberts, S. & Nowak, G., 2014. "Stabilizing the lasso against cross-validation variability," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 198-211.
  66. 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, vol. 71(C), pages 667-680.
  67. Luo, Ruiyan & Qi, Xin, 2015. "Sparse wavelet regression with multiple predictive curves," Journal of Multivariate Analysis, Elsevier, vol. 134(C), pages 33-49.
  68. Hyun Hak Kim & Norman Swanson, 2013. "Mining Big Data Using Parsimonious Factor and Shrinkage Methods," Departmental Working Papers 201316, Rutgers University, Department of Economics.
  69. Boriss Siliverstovs, 2015. "Dissecting the Purchasing Managers’ Index: Are all relevant components included? Are all included components relevant?," KOF Working papers 15-376, KOF Swiss Economic Institute, ETH Zurich.
  70. Lykou, Anastasia & Whittaker, Joe, 2010. "Sparse CCA using a Lasso with positivity constraints," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3144-3157, December.
  71. Zhou, Ding-Xuan, 2013. "On grouping effect of elastic net," Statistics & Probability Letters, Elsevier, vol. 83(9), pages 2108-2112.
  72. Abdallah Mkhadri & Mohamed Ouhourane, 2015. "A group VISA algorithm for variable selection," Statistical Methods and Applications, Springer, vol. 24(1), pages 41-60, March.
  73. Boriss Siliverstovs, 2015. "Short-term forecasting with mixed-frequency data: A MIDASSO approach," KOF Working papers 15-375, KOF Swiss Economic Institute, ETH Zurich.
  74. Yao, Weixin & Wang, Qin, 2013. "Robust variable selection through MAVE," Computational Statistics & Data Analysis, Elsevier, vol. 63(C), pages 42-49.
  75. Hu, Qinqin & Zeng, Peng & Lin, Lu, 2015. "The dual and degrees of freedom of linearly constrained generalized lasso," Computational Statistics & Data Analysis, Elsevier, vol. 86(C), pages 13-26.
  76. Wang, Xiaoming & Park, Taesung & Carriere, K.C., 2010. "Variable selection via combined penalization for high-dimensional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2230-2243, October.
  77. Huang, Zhensheng & Pang, Zhen & Lin, Bingqing & Shao, Quanxi, 2014. "Model structure selection in single-index-coefficient regression models," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 159-175.
  78. 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, vol. 55(3), pages 1342-1356, March.
  79. Wang, Tao & Zhu, Lixing, 2011. "Consistent tuning parameter selection in high dimensional sparse linear regression," Journal of Multivariate Analysis, Elsevier, vol. 102(7), pages 1141-1151, August.
  80. Wang, Mingqiu & Song, Lixin & Wang, Xiaoguang, 2010. "Bridge estimation for generalized linear models with a diverging number of parameters," Statistics & Probability Letters, Elsevier, vol. 80(21-22), pages 1584-1596, November.
  81. Shen, Haipeng & Huang, Jianhua Z., 2008. "Sparse principal component analysis via regularized low rank matrix approximation," Journal of Multivariate Analysis, Elsevier, vol. 99(6), pages 1015-1034, July.
  82. Hidetoshi Matsui & Toshihiro Misumi, 2015. "Variable selection for varying-coefficient models with the sparse regularization," Computational Statistics, Springer, vol. 30(1), pages 43-55, March.
  83. 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.
This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.