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Likelihood-Based Selection and Sharp Parameter Estimation

Citations

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Cited by:

  1. Luca Insolia & Ana Kenney & Martina Calovi & Francesca Chiaromonte, 2021. "Robust Variable Selection with Optimality Guarantees for High-Dimensional Logistic Regression," Stats, MDPI, vol. 4(3), pages 1-17, August.
  2. Yize Zhao & Matthias Chung & Brent A. Johnson & Carlos S. Moreno & Qi Long, 2016. "Hierarchical Feature Selection Incorporating Known and Novel Biological Information: Identifying Genomic Features Related to Prostate Cancer Recurrence," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1427-1439, October.
  3. Dai, Linlin & Chen, Kani & Sun, Zhihua & Liu, Zhenqiu & Li, Gang, 2018. "Broken adaptive ridge regression and its asymptotic properties," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 334-351.
  4. Ding, Hao & Qin, Shanshan & Wu, Yuehua & Wu, Yaohua, 2021. "Asymptotic properties on high-dimensional multivariate regression M-estimation," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
  5. Jianqing Fan & Yuan Liao & Han Liu, 2016. "An overview of the estimation of large covariance and precision matrices," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 1-32, February.
  6. Xiaotong Shen & Wei Pan & Yunzhang Zhu & Hui Zhou, 2013. "On constrained and regularized high-dimensional regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(5), pages 807-832, October.
  7. Yuan, Quan & Liu, Binghui, 2021. "Community detection via an efficient nonconvex optimization approach based on modularity," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
  8. Zhigeng Geng & Sijian Wang & Menggang Yu & Patrick O. Monahan & Victoria Champion & Grace Wahba, 2015. "Group variable selection via convex log-exp-sum penalty with application to a breast cancer survivor study," Biometrics, The International Biometric Society, vol. 71(1), pages 53-62, March.
  9. Faisal Maqbool Zahid & Shahla Faisal & Christian Heumann, 2020. "Variable selection techniques after multiple imputation in high-dimensional data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(3), pages 553-580, September.
  10. Yongli Zhang & Xiaotong Shen, 2015. "Adaptive Modeling Procedure Selection by Data Perturbation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 541-551, October.
  11. Chen, Shuo & Kang, Jian & Xing, Yishi & Zhao, Yunpeng & Milton, Donald K., 2018. "Estimating large covariance matrix with network topology for high-dimensional biomedical data," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 82-95.
  12. Kwon, Sunghoon & Oh, Seungyoung & Lee, Youngjo, 2016. "The use of random-effect models for high-dimensional variable selection problems," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 401-412.
  13. Luca Insolia & Ana Kenney & Francesca Chiaromonte & Giovanni Felici, 2022. "Simultaneous feature selection and outlier detection with optimality guarantees," Biometrics, The International Biometric Society, vol. 78(4), pages 1592-1603, December.
  14. Dong Liu & Changwei Zhao & Yong He & Lei Liu & Ying Guo & Xinsheng Zhang, 2023. "Simultaneous cluster structure learning and estimation of heterogeneous graphs for matrix‐variate fMRI data," Biometrics, The International Biometric Society, vol. 79(3), pages 2246-2259, September.
  15. Mehran Aflakparast & Mathisca de Gunst & Wessel van Wieringen, 2020. "Analysis of Twitter data with the Bayesian fused graphical lasso," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-28, July.
  16. McGillivray, Annaliza & Khalili, Abbas & Stephens, David A., 2020. "Estimating sparse networks with hubs," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
  17. Piotr Pokarowski & Wojciech Rejchel & Agnieszka Sołtys & Michał Frej & Jan Mielniczuk, 2022. "Improving Lasso for model selection and prediction," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 831-863, June.
  18. Zhihua Sun & Yi Liu & Kani Chen & Gang Li, 2022. "Broken adaptive ridge regression for right-censored survival data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(1), pages 69-91, February.
  19. Yunzhang Zhu & Xiaotong Shen & Wei Pan, 2014. "Structural Pursuit Over Multiple Undirected Graphs," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1683-1696, December.
  20. Chenchen Ma & Jing Ouyang & Gongjun Xu, 2023. "Learning Latent and Hierarchical Structures in Cognitive Diagnosis Models," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 175-207, March.
  21. Canhong Wen & Xueqin Wang & Shaoli Wang, 2015. "Laplace Error Penalty-based Variable Selection in High Dimension," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(3), pages 685-700, September.
  22. Xiaohui Liu & Yuanyuan Li & Jiming Jiang, 2021. "Simple measures of uncertainty for model selection," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 673-692, September.
  23. Yuezhang Che & Shuyan Chen & Xin Liu, 2022. "Sparse Index Tracking Portfolio with Sector Neutrality," Mathematics, MDPI, vol. 10(15), pages 1-22, July.
  24. Chen, Yang & Luo, Ziyan & Kong, Lingchen, 2021. "ℓ2,0-norm based selection and estimation for multivariate generalized linear models," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
  25. Shanshan Qin & Hao Ding & Yuehua Wu & Feng Liu, 2021. "High-dimensional sign-constrained feature selection and grouping," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(4), pages 787-819, August.
  26. Chun Wang & Jing Lu, 2021. "Learning Attribute Hierarchies From Data: Two Exploratory Approaches," Journal of Educational and Behavioral Statistics, , vol. 46(1), pages 58-84, February.
  27. Sunkyung Kim & Wei Pan & Xiaotong Shen, 2013. "Network-Based Penalized Regression With Application to Genomic Data," Biometrics, The International Biometric Society, vol. 69(3), pages 582-593, September.
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