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The Adaptive Lasso and Its Oracle Properties

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Cited by:

  1. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
  2. Jingyuan Liu & Runze Li & Rongling Wu, 2014. "Feature Selection for Varying Coefficient Models With Ultrahigh-Dimensional Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 266-274, March.
  3. Kwang Woo Ahn & Anjishnu Banerjee & Natasha Sahr & Soyoung Kim, 2018. "Group and within-group variable selection for competing risks data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(3), pages 407-424, July.
  4. Ai Ye & Kathleen M. Gates & Teague Rhine Henry & Lan Luo, 2021. "Path and Directionality Discovery in Individual Dynamic Models: A Regularized Unified Structural Equation Modeling Approach for Hybrid Vector Autoregression," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 404-441, June.
  5. Lu, Xun & Su, Liangjun, 2016. "Shrinkage estimation of dynamic panel data models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 190(1), pages 148-175.
  6. Jie Shen & Colin M. Gallagher & QiQi Lu, 2014. "Detection of multiple undocumented change-points using adaptive Lasso," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(6), pages 1161-1173, June.
  7. Du, Pang & Cheng, Guang & Liang, Hua, 2012. "Semiparametric regression models with additive nonparametric components and high dimensional parametric components," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2006-2017.
  8. Matthew Pietrosanu & Jueyu Gao & Linglong Kong & Bei Jiang & Di Niu, 2021. "Advanced algorithms for penalized quantile and composite quantile regression," Computational Statistics, Springer, vol. 36(1), pages 333-346, March.
  9. Juntao Wang & Yuan Li, 2023. "DINA Model with Entropy Penalization," Mathematics, MDPI, vol. 11(18), pages 1-16, September.
  10. Sierra A. Bainter & Thomas G. McCauley & Mahmoud M. Fahmy & Zachary T. Goodman & Lauren B. Kupis & J. Sunil Rao, 2023. "Comparing Bayesian Variable Selection to Lasso Approaches for Applications in Psychology," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 1032-1055, September.
  11. Ma, Shujie & Su, Liangjun, 2018. "Estimation of large dimensional factor models with an unknown number of breaks," Journal of Econometrics, Elsevier, vol. 207(1), pages 1-29.
  12. Andrea Bucci, 2020. "Realized Volatility Forecasting with Neural Networks," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.
  13. Panopoulou, Ekaterini & Vrontos, Spyridon, 2015. "Hedge fund return predictability; To combine forecasts or combine information?," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 103-122.
  14. Li, Xinjue & Zboňáková, Lenka & Wang, Weining & Härdle, Wolfgang Karl, 2019. "Combining Penalization and Adaption in High Dimension with Application in Bond Risk Premia Forecasting," IRTG 1792 Discussion Papers 2019-030, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  15. Alexander M. Chinco & Adam D. Clark-Joseph & Mao Ye, 2017. "Sparse Signals in the Cross-Section of Returns," NBER Working Papers 23933, National Bureau of Economic Research, Inc.
  16. Yang, Yaohong & Zhao, Weihua & Wang, Lei, 2023. "Online regularized matrix regression with streaming data," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
  17. Margherita Giuzio, 2017. "Genetic algorithm versus classical methods in sparse index tracking," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 40(1), pages 243-256, November.
  18. Wang, Weiwei & Wu, Xianyi & Zhao, Xiaobing & Zhou, Xian, 2018. "Robust variable selection of joint frailty model for panel count data," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 60-78.
  19. Xu, Yang & Zhao, Shishun & Hu, Tao & Sun, Jianguo, 2021. "Variable selection for generalized odds rate mixture cure models with interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
  20. Liang, Chong & Schienle, Melanie, 2019. "Determination of vector error correction models in high dimensions," Journal of Econometrics, Elsevier, vol. 208(2), pages 418-441.
  21. Alessandra Amendola & Francesco Giordano & Maria Lucia Parrella & Marialuisa Restaino, 2017. "Variable selection in high‐dimensional regression: a nonparametric procedure for business failure prediction," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(4), pages 355-368, August.
  22. Zakariya Yahya Algamal & Muhammad Hisyam Lee, 2019. "A two-stage sparse logistic regression for optimal gene selection in high-dimensional microarray data classification," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 753-771, September.
  23. Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023. "Big data forecasting of South African inflation," Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
  24. Elena Geminiani & Giampiero Marra & Irini Moustaki, 2021. "Single- and Multiple-Group Penalized Factor Analysis: A Trust-Region Algorithm Approach with Integrated Automatic Multiple Tuning Parameter Selection," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 65-95, March.
  25. Wang, Kangning & Li, Shaomin & Sun, Xiaofei & Lin, Lu, 2019. "Modal regression statistical inference for longitudinal data semivarying coefficient models: Generalized estimating equations, empirical likelihood and variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 257-276.
  26. Giurcanu, Mihai C., 2012. "Bootstrapping in non-regular smooth function models," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 78-93.
  27. Belaïd, Fateh & Garcia, Thomas, 2016. "Understanding the spectrum of residential energy-saving behaviours: French evidence using disaggregated data," Energy Economics, Elsevier, vol. 57(C), pages 204-214.
  28. Liuquan Sun & Shuwei Li & Lianming Wang & Xinyuan Song & Xuemei Sui, 2022. "Simultaneous variable selection in regression analysis of multivariate interval‐censored data," Biometrics, The International Biometric Society, vol. 78(4), pages 1402-1413, December.
  29. Kwon, Sunghoon & Choi, Hosik & Kim, Yongdai, 2011. "Quadratic approximation on SCAD penalized estimation," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 421-428, January.
  30. Joel L. Horowitz & Jian Huang, 2012. "Penalized estimation of high-dimensional models under a generalized sparsity condition," CeMMAP working papers CWP17/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  31. Patrick Breheny, 2015. "The group exponential lasso for bi‐level variable selection," Biometrics, The International Biometric Society, vol. 71(3), pages 731-740, September.
  32. Thilo Reinschlussel & Martin C. Arnold, 2024. "Information-Enriched Selection of Stationary and Non-Stationary Autoregressions using the Adaptive Lasso," Papers 2402.16580, arXiv.org.
  33. Breinlich, Holger & Corradi, Valentina & Rocha, Nadia & Ruta, Michele & Silva, J.M.C. Santos & Zylkin, Tom, 2021. "Machine learning in international trade research - evaluating the impact of trade agreements," LSE Research Online Documents on Economics 114379, London School of Economics and Political Science, LSE Library.
  34. Emmanouil Androulakis & Christos Koukouvinos & Kalliopi Mylona & Filia Vonta, 2010. "A real survival analysis application via variable selection methods for Cox's proportional hazards model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(8), pages 1399-1406.
  35. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
  36. Miyazaki, Izuru, 2023. "Recovery of partly sparse and dense signals," Journal of Multivariate Analysis, Elsevier, vol. 195(C).
  37. Ziqi Chen & Chenlei Leng, 2016. "Dynamic Covariance Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1196-1207, July.
  38. Zhang, Yan-Qing & Tian, Guo-Liang & Tang, Nian-Sheng, 2016. "Latent variable selection in structural equation models," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 190-205.
  39. Xuerui Gao & Yanqin Bai & Qian Li, 2021. "A sparse optimization problem with hybrid $$L_2{\text {-}}L_p$$ L 2 - L p regularization for application of magnetic resonance brain images," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 760-784, November.
  40. Diego Vidaurre & Concha Bielza & Pedro Larrañaga, 2012. "Lazy lasso for local regression," Computational Statistics, Springer, vol. 27(3), pages 531-550, September.
  41. Zhong, Yan & Sang, Huiyan & Cook, Scott J. & Kellstedt, Paul M., 2023. "Sparse spatially clustered coefficient model via adaptive regularization," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
  42. Bahadır Yüzbaşı & S. Ejaz Ahmed, 2020. "Ridge Type Shrinkage Estimation of Seemingly Unrelated Regressions And Analytics of Economic and Financial Data from “Fragile Five” Countries," JRFM, MDPI, vol. 13(6), pages 1-19, June.
  43. Sumin Hou & Hao Lv, 2023. "A Group MCP Approach for Structure Identification in Non-Parametric Accelerated Failure Time Additive Regression Model," Mathematics, MDPI, vol. 11(22), pages 1-14, November.
  44. Caner, Mehmet & Kock, Anders Bredahl, 2018. "Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative Lasso," Journal of Econometrics, Elsevier, vol. 203(1), pages 143-168.
  45. Jianqing Fan & Yang Feng & Jiancheng Jiang & Xin Tong, 2016. "Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 275-287, March.
  46. de Paula, Aureo & Rasul, Imran & Souza, Pedro, 2018. "Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition," CEPR Discussion Papers 12792, C.E.P.R. Discussion Papers.
  47. Liqian Cai & Arnab Bhattacharjee & Roger Calantone & Taps Maiti, 2019. "Variable Selection with Spatially Autoregressive Errors: A Generalized Moments LASSO Estimator," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 146-200, September.
  48. Liu, Yanyan & Zhang, Jing & Zhao, Xingqiu, 2018. "A new nonparametric screening method for ultrahigh-dimensional survival data," Computational Statistics & Data Analysis, Elsevier, vol. 119(C), pages 74-85.
  49. Li, Dan & Li, Yijun & Wang, Chaoqun & Chen, Min & Wu, Qi, 2023. "Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks," Applied Energy, Elsevier, vol. 331(C).
  50. Steffen Ventz & Rahul Mazumder & Lorenzo Trippa, 2022. "Integration of survival data from multiple studies," Biometrics, The International Biometric Society, vol. 78(4), pages 1365-1376, December.
  51. Anestis Antoniadis & Irène Gijbels & Mila Nikolova, 2011. "Penalized likelihood regression for generalized linear models with non-quadratic penalties," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(3), pages 585-615, June.
  52. Alain Hecq & Luca Margaritella & Stephan Smeekes, 2023. "Granger Causality Testing in High-Dimensional VARs: A Post-Double-Selection Procedure," Journal of Financial Econometrics, Oxford University Press, vol. 21(3), pages 915-958.
  53. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
  54. Yongdai Kim & Jong-June Jeon & Sangmi Han, 2016. "A Necessary Condition for the Strong Oracle Property," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 610-624, June.
  55. An, Baiguo & Guo, Jianhua & Wang, Hansheng, 2013. "Multivariate regression shrinkage and selection by canonical correlation analysis," Computational Statistics & Data Analysis, Elsevier, vol. 62(C), pages 93-107.
  56. Wang, Kangning & Li, Shaomin, 2021. "Robust distributed modal regression for massive data," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
  57. Delle-Monache, Davide & De-Polis, Andrea & Petrella, Ivan, 2020. "Modelling and Forecasting Macroeconomic Downside Risk," EMF Research Papers 34, Economic Modelling and Forecasting Group.
  58. Qifan Song & Guang Cheng, 2020. "Bayesian Fusion Estimation via t Shrinkage," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(2), pages 353-385, August.
  59. Yi Zhao & Lexin Li & Brian S. Caffo, 2021. "Multimodal neuroimaging data integration and pathway analysis," Biometrics, The International Biometric Society, vol. 77(3), pages 879-889, September.
  60. He, Yong & Zhang, Liang & Ji, Jiadong & Zhang, Xinsheng, 2019. "Robust feature screening for elliptical copula regression model," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 568-582.
  61. Cox Lwaka Tamba & Yuan-Li Ni & Yuan-Ming Zhang, 2017. "Iterative sure independence screening EM-Bayesian LASSO algorithm for multi-locus genome-wide association studies," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-20, January.
  62. Ying Huang & Shibasish Dasgupta, 2019. "Likelihood-Based Methods for Assessing Principal Surrogate Endpoints in Vaccine Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(3), pages 504-523, December.
  63. Carvalho, Carlos & Masini, Ricardo & Medeiros, Marcelo C., 2018. "ArCo: An artificial counterfactual approach for high-dimensional panel time-series data," Journal of Econometrics, Elsevier, vol. 207(2), pages 352-380.
  64. Ziel, Florian, 2016. "Iteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to AR–ARCH type processes," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 773-793.
  65. Ciner, Cetin, 2021. "Stock return predictability in the time of COVID-19," Finance Research Letters, Elsevier, vol. 38(C).
  66. Yoonsuh Jung, 2018. "Multiple predicting K-fold cross-validation for model selection," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(1), pages 197-215, January.
  67. Hu Yang & Huilan Liu, 2016. "Penalized weighted composite quantile estimators with missing covariates," Statistical Papers, Springer, vol. 57(1), pages 69-88, March.
  68. Ren, Yunwen & Zhang, Xinsheng, 2010. "Subset selection for vector autoregressive processes via adaptive Lasso," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1705-1712, December.
  69. Maayan Yitshak-Sade & M. Patricia Fabian & Kevin J. Lane & Jaime E. Hart & Joel D. Schwartz & Francine Laden & Peter James & Kelvin C. Fong & Itai Kloog & Antonella Zanobetti, 2020. "Estimating the Combined Effects of Natural and Built Environmental Exposures on Birthweight among Urban Residents in Massachusetts," IJERPH, MDPI, vol. 17(23), pages 1-16, November.
  70. Qingliang Fan & Wei Zhong, 2018. "Nonparametric Additive Instrumental Variable Estimator: A Group Shrinkage Estimation Perspective," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(3), pages 388-399, July.
  71. Fang, Xiaolei & Paynabar, Kamran & Gebraeel, Nagi, 2017. "Multistream sensor fusion-based prognostics model for systems with single failure modes," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 322-331.
  72. Wolfgang Karl Härdle & David Kuo Chuen Lee & Sergey Nasekin & Alla Petukhina, 2018. "Tail Event Driven ASset allocation: evidence from equity and mutual funds’ markets," Journal of Asset Management, Palgrave Macmillan, vol. 19(1), pages 49-63, January.
  73. Linton, Oliver & Seo, Myung Hwan & Whang, Yoon-Jae, 2023. "Testing stochastic dominance with many conditioning variables," Journal of Econometrics, Elsevier, vol. 235(2), pages 507-527.
  74. Zhang, Ting & Wang, Lei, 2020. "Smoothed empirical likelihood inference and variable selection for quantile regression with nonignorable missing response," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
  75. Yanhang Zhang & Junxian Zhu & Jin Zhu & Xueqin Wang, 2023. "A Splicing Approach to Best Subset of Groups Selection," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 104-119, January.
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  77. Yanfang Zhang & Chuanhua Wei & Xiaolin Liu, 2022. "Group Logistic Regression Models with l p,q Regularization," Mathematics, MDPI, vol. 10(13), pages 1-15, June.
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  80. Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Estimating global bank network connectedness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(1), pages 1-15, January.
  81. Susan M. Shortreed & Ashkan Ertefaie, 2017. "Outcome‐adaptive lasso: Variable selection for causal inference," Biometrics, The International Biometric Society, vol. 73(4), pages 1111-1122, December.
  82. Huang, Xianzheng & Zhang, Hongmei, 2021. "Tests for differential Gaussian Bayesian networks based on quadratic inference functions," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
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  121. Zhang, Jing & Liu, Yanyan & Wu, Yuanshan, 2017. "Correlation rank screening for ultrahigh-dimensional survival data," Computational Statistics & Data Analysis, Elsevier, vol. 108(C), pages 121-132.
  122. Dengke Xu & Zhongzhan Zhang & Liucang Wu, 2014. "Variable selection in high-dimensional double generalized linear models," Statistical Papers, Springer, vol. 55(2), pages 327-347, May.
  123. Olivier Fortin‐Gagnon & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "A large Canadian database for macroeconomic analysis," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(4), pages 1799-1833, November.
  124. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
  125. Ismail Shah & Hina Naz & Sajid Ali & Amani Almohaimeed & Showkat Ahmad Lone, 2023. "A New Quantile-Based Approach for LASSO Estimation," Mathematics, MDPI, vol. 11(6), pages 1-13, March.
  126. Li-Pang Chen, 2021. "Feature screening based on distance correlation for ultrahigh-dimensional censored data with covariate measurement error," Computational Statistics, Springer, vol. 36(2), pages 857-884, June.
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