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Bayesian adaptive Lasso

Citations

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Cited by:

  1. 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.
  2. Ando, Tomohiro & Bai, Jushan & Li, Kunpeng, 2022. "Bayesian and maximum likelihood analysis of large-scale panel choice models with unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 230(1), pages 20-38.
  3. Ma, Xiaohua & Gao, Qibing & Wang, Jun & Wang, Mingquan & Zhu, Chunhua, 2026. "A Bayesian synthetic control method via horseshoe priors," Economic Modelling, Elsevier, vol. 157(C).
  4. Paponpat Taveeapiradeecharoen & Popkarn Arwatchanakarn, 2025. "Forecasting Thai inflation from univariate Bayesian regression perspective," Papers 2505.05334, arXiv.org, revised May 2025.
  5. Mogliani, Matteo & Simoni, Anna, 2021. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Journal of Econometrics, Elsevier, vol. 222(1), pages 833-860.
  6. Sandra Stankiewicz, 2015. "Forecasting Euro Area Macroeconomic Variables with Bayesian Adaptive Elastic Net," Working Paper Series of the Department of Economics, University of Konstanz 2015-12, Department of Economics, University of Konstanz.
  7. Gallant, A. Ronald & Hong, Han & Leung, Michael P. & Li, Jessie, 2022. "Constrained estimation using penalization and MCMC," Journal of Econometrics, Elsevier, vol. 228(1), pages 85-106.
  8. D. Calvetti & E. Somersalo, 2025. "Distributed Tikhonov regularization for ill-posed inverse problems from a Bayesian perspective," Computational Optimization and Applications, Springer, vol. 91(2), pages 541-572, June.
  9. Djibril Ndiaye & Khader Khadraoui, 2025. "Bayesian Adaptive Variable Selection with a Generalized g-prior," Methodology and Computing in Applied Probability, Springer, vol. 27(4), pages 1-30, December.
  10. Larissa C. Alves & Ronaldo Dias & Helio S. Migon, 2024. "Variational Bayesian Lasso for spline regression," Computational Statistics, Springer, vol. 39(4), pages 2039-2064, June.
  11. Mallick, Himel & Yi, Nengjun, 2017. "Bayesian group bridge for bi-level variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 115-133.
  12. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Foundations and Trends(R) in Econometrics, now publishers, vol. 11(4), pages 230-354, June.
  13. Xiangyu Shi & Xiangyang Xu & Ruiyuan Cao & Tianfa Xie, 2026. "Bayesian variable selection and estimation based on asymmetric squared loss in high dimensions," Computational Statistics, Springer, vol. 41(1), pages 1-30, January.
  14. Yong Li & Hefei Liu & Rubing Li, 2023. "Study of Bayesian variable selection method on mixed linear regression models," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-13, March.
  15. Xianyi Wu & Xian Zhou, 2019. "On Hodges’ superefficiency and merits of oracle property in model selection," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(5), pages 1093-1119, October.
  16. Posch, Konstantin & Arbeiter, Maximilian & Pilz, Juergen, 2020. "A novel Bayesian approach for variable selection in linear regression models," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
  17. Na Shan & Ping-Feng Xu, 2025. "Bayesian Adaptive Lasso for the Detection of Differential Item Functioning in Graded Response Models," Journal of Educational and Behavioral Statistics, , vol. 50(2), pages 187-213, April.
  18. Sakae Oya, 2021. "A Bayesian Graphical Approach for Large-Scale Portfolio Management with Fewer Historical Data," Papers 2103.05880, arXiv.org, revised Mar 2022.
  19. 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.
  20. Feng, Xiangnan & Lu, Bin & Song, Xinyuan & Ma, Shuang, 2019. "Financial literacy and household finances: A Bayesian two-part latent variable modeling approach," Journal of Empirical Finance, Elsevier, vol. 51(C), pages 119-137.
  21. Zhao, Weihua & Lian, Heng & Zhang, Riquan & Lai, Peng, 2016. "Estimation and variable selection for proportional response data with partially linear single-index models," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 40-56.
  22. Feng, Xiang-Nan & Wang, Yifan & Lu, Bin & Song, Xin-Yuan, 2017. "Bayesian regularized quantile structural equation models," Journal of Multivariate Analysis, Elsevier, vol. 154(C), pages 234-248.
  23. Chowdhury, K.P., 2023. "Nonparametric functional analysis under joint estimation with applications to identifying highly cited papers," Journal of Informetrics, Elsevier, vol. 17(4).
  24. Mauro Bernardi & Daniele Bianchi & Nicolas Bianco, 2024. "Variational Inference for Large Bayesian Vector Autoregressions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 1066-1082, July.
  25. Na Shan & Ping-Feng Xu, 2024. "Bayesian Adaptive Lasso for Detecting Item–Trait Relationship and Differential Item Functioning in Multidimensional Item Response Theory Models," Psychometrika, Springer;The Psychometric Society, vol. 89(4), pages 1337-1365, December.
  26. Sakae Oya, 2022. "A Bayesian Graphical Approach for Large-Scale Portfolio Management with Fewer Historical Data," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(3), pages 507-526, September.
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