Stability selection

Citations

Cited by:

1. Wang, Ke & Franks, Alexander & Oh, Sang-Yun, 2023. "Learning Gaussian graphical models with latent confounders," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
2. Elyasiani, Elyas & Movaghari, Hadi, 2022. "Determinants of corporate cash holdings: An application of a robust variable selection technique," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 967-993.
3. Solari, Aldo & Djordjilović, Vera, 2022. "Multi split conformal prediction," Statistics & Probability Letters, Elsevier, vol. 184(C).
4. Gautier Marti & Frank Nielsen & Philippe Donnat & S'ebastien Andler, 2016. "On clustering financial time series: a need for distances between dependent random variables," Papers 1603.07822, arXiv.org.
5. Qin, Yichen & Wang, Linna & Li, Yang & Li, Rong, 2023. "Visualization and assessment of model selection uncertainty," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
6. Diego Vidaurre & Concha Bielza & Pedro Larrañaga, 2013. "A Survey of L1 Regression," International Statistical Review, International Statistical Institute, vol. 81(3), pages 361-387, December.
7. Vaughan, Gregory, 2020. "Efficient big data model selection with applications to fraud detection," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1116-1127.
8. Soyeon Kim & Veerabhadran Baladandayuthapani & J. Jack Lee, 2017. "Prediction-Oriented Marker Selection (PROMISE): With Application to High-Dimensional Regression," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 217-245, June.
9. Wang, Qing & Lindsay, Bruce G., 2015. "Improving cross-validated bandwidth selection using subsampling-extrapolation techniques," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 51-71.
10. Skripnikov, A. & Michailidis, G., 2019. "Joint estimation of multiple network Granger causal models," Econometrics and Statistics, Elsevier, vol. 10(C), pages 120-133.
11. Skripnikov, A. & Michailidis, G., 2019. "Regularized joint estimation of related vector autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 164-177.
12. Milosh, Maria & Painter, Marcus & Sonin, Konstantin & Van Dijcke, David & Wright, Austin L., 2021. "Unmasking partisanship: Polarization undermines public response to collective risk," Journal of Public Economics, Elsevier, vol. 204(C).
    • Maria Milosh & Marcus Painter & Konstantin Sonin & David Van Dijcke & Austin L. Wright, 2020. "Unmasking Partisanship: Polarization Undermines Public Response to Collective Risk," Working Papers 2020-102, Becker Friedman Institute for Research In Economics.
    • Sonin, Konstantin & Milosh, Maria & Painter, Marcus & Van Dijcke, David & Wright, Austin L., 2020. "Unmasking Partisanship: Polarization Undermines Public Response to Collective Risk," CEPR Discussion Papers 15464, C.E.P.R. Discussion Papers.
13. Yang, Yihe & Zhou, Jie & Pan, Jianxin, 2021. "Estimation and optimal structure selection of high-dimensional Toeplitz covariance matrix," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
14. Wei, Jie & Chen, Hui, 2020. "Determining the number of factors in approximate factor models by twice K-fold cross validation," Economics Letters, Elsevier, vol. 191(C).
15. Cordoni, Francesco & Dorémus, Nicolas & Moneta, Alessio, 2024. "Identification of vector autoregressive models with nonlinear contemporaneous structure," Journal of Economic Dynamics and Control, Elsevier, vol. 162(C).
    • Francesco Cordoni & Nicolas Doremus & Alessio Moneta, 2023. "Identification of Vector Autoregressive Models with Nonlinear Contemporaneous Structure," LEM Papers Series 2023/07, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
16. Jin, Sainan & Miao, Ke & Su, Liangjun, 2021. "On factor models with random missing: EM estimation, inference, and cross validation," Journal of Econometrics, Elsevier, vol. 222(1), pages 745-777.
    • Su, Liangjun & Miao, Ke & Jin, Sainan, 2019. "On Factor Models with Random Missing: EM Estimation, Inference, and Cross Validation," Economics and Statistics Working Papers 4-2019, Singapore Management University, School of Economics.
17. Emilie Devijver, 2017. "Model-based regression clustering for high-dimensional data: application to functional data," 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. 11(2), pages 243-279, June.
18. Jeon, Jong-June & Kim, Yongdai & Won, Sungho & Choi, Hosik, 2020. "Primal path algorithm for compositional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 148(C).
19. Cho, Youngjoo & Zhan, Xiang & Ghosh, Debashis, 2022. "Nonlinear predictive directions in clinical trials," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
20. Sabyasachi Kar & Amaani Bashir & Mayank Jain, 2021. "New Approaches to Forecasting Growth and Inflation: Big Data and Machine Learning," IEG Working Papers 446, Institute of Economic Growth.
21. Mareckova, Jana & Pohlmeier, Winfried, 2017. "Noncognitive Skills and Labor Market Outcomes: A Machine Learning Approach," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168195, Verein für Socialpolitik / German Economic Association.
22. Giordano, Francesco & Milito, Sara & Parrella, Maria Lucia, 2023. "Linear and nonlinear effects explaining the risk of Covid-19 infection: an empirical analysis on real data from the USA," Socio-Economic Planning Sciences, Elsevier, vol. 90(C).
23. Jayachandran, Seema & Biradavolu, Monica & Cooper, Jan, 2023. "Using machine learning and qualitative interviews to design a five-question survey module for women’s agency," World Development, Elsevier, vol. 161(C).
24. Tian, Shaonan & Yu, Yan & Guo, Hui, 2015. "Variable selection and corporate bankruptcy forecasts," Journal of Banking & Finance, Elsevier, vol. 52(C), pages 89-100.
25. Roberts, S. & Nowak, G., 2014. "Stabilizing the lasso against cross-validation variability," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 198-211.
26. Yu, Dengdeng & Zhang, Li & Mizera, Ivan & Jiang, Bei & Kong, Linglong, 2019. "Sparse wavelet estimation in quantile regression with multiple functional predictors," Computational Statistics & Data Analysis, Elsevier, vol. 136(C), pages 12-29.
27. Raheem, S.M. Enayetur & Ahmed, S. Ejaz & Doksum, Kjell A., 2012. "Absolute penalty and shrinkage estimation in partially linear models," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 874-891.
28. Joel L. Horowitz, 2015. "Variable selection and estimation in high-dimensional models," Canadian Journal of Economics, Canadian Economics Association, vol. 48(2), pages 389-407, May.
29. Besse, Philippe & Leconte, Eve & Walschaerts, Marie, 2012. "Stable variable selection for right censored data: comparison of methods," TSE Working Papers 12-486, Toulouse School of Economics (TSE).
30. Wang, Junhui & Fang, Yixin, 2013. "Analysis of presence-only data via semi-supervised learning approaches," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 134-143.
31. Hsu, David, 2015. "Identifying key variables and interactions in statistical models of building energy consumption using regularization," Energy, Elsevier, vol. 83(C), pages 144-155.
32. Jessie Jeng, X., 2016. "Detecting weak signals in high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 147(C), pages 234-246.
33. Huang, Shih-Ting & Xie, Fang & Lederer, Johannes, 2021. "Tuning-free ridge estimators for high-dimensional generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
34. Nazemi, Abdolreza & Fabozzi, Frank J., 2018. "Macroeconomic variable selection for creditor recovery rates," Journal of Banking & Finance, Elsevier, vol. 89(C), pages 14-25.
35. Tan, Xin Lu, 2019. "Optimal estimation of slope vector in high-dimensional linear transformation models," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 179-204.
36. Sohrabi, Narges & Movaghari, Hadi, 2020. "Reliable factors of Capital structure: Stability selection approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 296-310.
37. Joel L. Horowitz, 2015. "Variable selection and estimation in high‐dimensional models," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 48(2), pages 389-407, May.
38. Chun, Hyonho & Lee, Myung Hee & Fleet, James C. & Oh, Ji Hwan, 2016. "Graphical models via joint quantile regression with component selection," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 162-171.
39. Latouche, Pierre & Mattei, Pierre-Alexandre & Bouveyron, Charles & Chiquet, Julien, 2016. "Combining a relaxed EM algorithm with Occam’s razor for Bayesian variable selection in high-dimensional regression," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 177-190.
40. Gautier Marti & Philippe Very & Philippe Donnat, 2015. "Toward a generic representation of random variables for machine learning," Working Papers hal-01196883, HAL.
41. Wang, Jue & Zhou, Hao & Hong, Tao & Li, Xiang & Wang, Shouyang, 2020. "A multi-granularity heterogeneous combination approach to crude oil price forecasting," Energy Economics, Elsevier, vol. 91(C).
42. Tan, Kean Ming & Witten, Daniela & Shojaie, Ali, 2015. "The cluster graphical lasso for improved estimation of Gaussian graphical models," Computational Statistics & Data Analysis, Elsevier, vol. 85(C), pages 23-36.
43. Capanu, Marinela & Giurcanu, Mihai & Begg, Colin B. & Gönen, Mithat, 2023. "Subsampling based variable selection for generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 184(C).
44. Guo, Peiyang & Lam, Jacqueline C.K. & Li, Victor O.K., 2019. "Drivers of domestic electricity users’ price responsiveness: A novel machine learning approach," Applied Energy, Elsevier, vol. 235(C), pages 900-913.
45. Joel L. Horowitz, 2015. "Variable selection and estimation in high-dimensional models," CeMMAP working papers CWP35/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
46. De Bin, Riccardo & Boulesteix, Anne-Laure & Sauerbrei, Willi, 2017. "Detection of influential points as a byproduct of resampling-based variable selection procedures," Computational Statistics & Data Analysis, Elsevier, vol. 116(C), pages 19-31.
47. Du, Lilun & Lan, Wei & Luo, Ronghua & Zhong, Pingshou, 2018. "Factor-adjusted multiple testing of correlations," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 34-47.
48. Liang, Lixing & Zhuang, Yipeng & Yu, Philip L.H., 2024. "Variable selection for high-dimensional incomplete data," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
49. Dallakyan, Aramayis & Kim, Rakheon & Pourahmadi, Mohsen, 2022. "Time series graphical lasso and sparse VAR estimation," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).
50. Gillard, Jonathan & Zhigljavsky, Anatoly, 2018. "Optimal estimation of direction in regression models with large number of parameters," Applied Mathematics and Computation, Elsevier, vol. 318(C), pages 281-289.