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The horseshoe estimator for sparse signals

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Cited by:

  1. Hauzenberger, Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2025. "Bayesian neural networks for macroeconomic analysis," Journal of Econometrics, Elsevier, vol. 249(PC).
  2. Risto Heikkinen & Juha Karvanen & Kaisa Miettinen, 2025. "A Bayesian model for portfolio decisions based on debiased and regularized expert predictions," Journal of Business Economics, Springer, vol. 95(5), pages 669-706, July.
  3. Naveen Naidu Narisetty, 2020. "Discussion," International Statistical Review, International Statistical Institute, vol. 88(2), pages 330-334, August.
  4. Monica Billio & Roberto Casarin & Matteo Iacopini & Sylvia Kaufmann, 2023. "Bayesian Dynamic Tensor Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(2), pages 429-439, April.
  5. Jan Prüser & Florian Huber, 2024. "Nonlinearities in macroeconomic tail risk through the lens of big data quantile regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 269-291, March.
  6. Huber, Florian & Onorante, Luca & Pfarrhofer, Michael, 2024. "Forecasting euro area inflation using a huge panel of survey expectations," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1042-1054.
  7. Hauzenberger Niko & Huber Florian & Koop Gary, 2024. "Dynamic Shrinkage Priors for Large Time-Varying Parameter Regressions Using Scalable Markov Chain Monte Carlo Methods," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(2), pages 201-225, April.
  8. Ley, Eduardo & Steel, Mark F.J., 2012. "Mixtures of g-priors for Bayesian model averaging with economic applications," Journal of Econometrics, Elsevier, vol. 171(2), pages 251-266.
  9. Anwen Yin, 2022. "Does the kitchen‐sink model work forecasting the equity premium?," International Review of Finance, International Review of Finance Ltd., vol. 22(1), pages 223-247, March.
  10. Joshua Lukemire & Giuseppe Pagnoni & Ying Guo, 2023. "Sparse Bayesian modeling of hierarchical independent component analysis: Reliable estimation of individual differences in brain networks," Biometrics, The International Biometric Society, vol. 79(4), pages 3599-3611, December.
  11. P. Richard Hahn & Carlos M. Carvalho, 2015. "Decoupling Shrinkage and Selection in Bayesian Linear Models: A Posterior Summary Perspective," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 435-448, March.
  12. Anindya Bhadra & Jyotishka Datta & Nicholas G. Polson & Brandon T. Willard, 2021. "The Horseshoe-Like Regularization for Feature Subset Selection," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 185-214, May.
  13. Atchadé, Yves F., 2019. "Quasi-Bayesian estimation of large Gaussian graphical models," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 656-671.
  14. McLain, Alexander C. & Zgodic, Anja & Bondell, Howard, 2025. "Efficient sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 207(C).
  15. Debamita Kundu & Riten Mitra & Jeremy T. Gaskins, 2021. "Bayesian variable selection for multioutcome models through shared shrinkage," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 295-320, March.
  16. Sourish Das & Rituparna Sen, 2021. "Sparse Portfolio Selection via Bayesian Multiple Testing," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 585-617, November.
  17. Davide Delle Monache & Andrea De Polis & Ivan Petrella, 2024. "Modeling and Forecasting Macroeconomic Downside Risk," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 1010-1025, July.
  18. 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.
  19. Baltodano López Ovielt & Bulfone Giacomo & Casarin Roberto & Ravazzolo Francesco, 2024. "Modeling Corporate CDS Spreads Using Markov Switching Regressions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(2), pages 271-292, April.
  20. Shubhajit Sen & Damitri Kundu & Kiranmoy Das, 2024. "A flexible Bayesian variable selection approach for modeling interval data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(1), pages 267-286, March.
  21. Gefang, Deborah & Koop, Gary & Poon, Aubrey, 2023. "Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage," International Journal of Forecasting, Elsevier, vol. 39(1), pages 346-363.
  22. Kohn, Robert & Nguyen, Nghia & Nott, David & Tran, Minh-Ngoc, 2017. "Random Effects Models with Deep Neural Network Basis Functions: Methodology and Computation," Working Papers 2123/17877, University of Sydney Business School, Discipline of Business Analytics.
  23. Christos Merkatas & Simo Särkkä, 2023. "System identification using autoregressive Bayesian neural networks with nonparametric noise models," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(3), pages 319-330, May.
  24. Sylvia Fruhwirth-Schnatter & Peter Knaus, 2022. "Sparse Bayesian State-Space and Time-Varying Parameter Models," Papers 2207.12147, arXiv.org.
  25. Yi Nengjun & Ma Shuangge, 2012. "Hierarchical Shrinkage Priors and Model Fitting for High-dimensional Generalized Linear Models," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(6), pages 1-25, November.
  26. Shi, Guiling & Lim, Chae Young & Maiti, Tapabrata, 2019. "Model selection using mass-nonlocal prior," Statistics & Probability Letters, Elsevier, vol. 147(C), pages 36-44.
  27. Annalisa Cadonna & Sylvia Fruhwirth-Schnatter & Peter Knaus, 2019. "Triple the gamma -- A unifying shrinkage prior for variance and variable selection in sparse state space and TVP models," Papers 1912.03100, arXiv.org.
  28. Korobilis, Dimitris & Schröder, Maximilian, 2025. "Monitoring multi-country macroeconomic risk: A quantile factor-augmented vector autoregressive (QFAVAR) approach," Journal of Econometrics, Elsevier, vol. 249(PC).
  29. Joshua C. C. Chan & Gary Koop & Xuewen Yu, 2024. "Large Order-Invariant Bayesian VARs with Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(2), pages 825-837, April.
  30. Deborah Gefang & Gary Koop & Aubrey Poon, 2019. "Variational Bayesian Inference in Large Vector Autoregressions with Hierarchical Shrinkage," Discussion Papers in Economics 19/05, Division of Economics, School of Business, University of Leicester.
  31. 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.
  32. Agnese Maria Di Brisco & Sonia Migliorati, 2021. "A spatial mixed-effects regression model for electoral data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(2), pages 543-571, June.
  33. Qihui Chen & Zheng Fang & Ruixuan Liu, 2025. "Debiased Bayesian Inference for High-dimensional Regression Models," Papers 2512.09257, arXiv.org.
  34. Daniel Mork & Ander Wilson, 2023. "Estimating perinatal critical windows of susceptibility to environmental mixtures via structured Bayesian regression tree pairs," Biometrics, The International Biometric Society, vol. 79(1), pages 449-461, March.
  35. Arnab Kumar Maity & Sanjib Basu & Santu Ghosh, 2021. "Bayesian criterion‐based variable selection," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 835-857, August.
  36. van Erp, Sara & Oberski, Daniel L. & Mulder, Joris, 2018. "Shrinkage priors for Bayesian penalized regression," OSF Preprints cg8fq, Center for Open Science.
  37. David Kohns & Tibor Szendrei, 2021. "Decoupling Shrinkage and Selection for the Bayesian Quantile Regression," Papers 2107.08498, arXiv.org.
  38. Michael Bergrab & Christian Aßmann, 2024. "Automated Bayesian variable selection methods for binary regression models with missing covariate data," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 18(2), pages 203-244, June.
  39. Anja Zgodic & Ray Bai & Jiajia Zhang & Yuan Wang & Christopher Rorden & Alexander C. McLain, 2026. "Quantifying predictive uncertainty of aphasia severity in stroke patients with sparse heteroscedastic Bayesian high-dimensional regression," Computational Statistics, Springer, vol. 41(1), pages 1-23, January.
  40. Bai, Ray & Ghosh, Malay, 2018. "High-dimensional multivariate posterior consistency under global–local shrinkage priors," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 157-170.
  41. Michael Pfarrhofer, 2024. "Forecasts with Bayesian vector autoregressions under real time conditions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 771-801, April.
  42. Peter Knaus & Sylvia Fruhwirth-Schnatter, 2023. "The Dynamic Triple Gamma Prior as a Shrinkage Process Prior for Time-Varying Parameter Models," Papers 2312.10487, arXiv.org, revised Jan 2025.
  43. Katz, Harrison & Brusch, Kai Thomas & Weiss, Robert E., 2024. "A Bayesian Dirichlet auto-regressive moving average model for forecasting lead times," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1556-1567.
  44. Zhen Yu & Keming Yu & Wolfgang K. Härdle & Xueliang Zhang & Kai Wang & Maozai Tian, 2022. "Bayesian spatio‐temporal modeling for the inpatient hospital costs of alcohol‐related disorders," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 644-667, December.
  45. Robert B. Gramacy, 2020. "Discussion," International Statistical Review, International Statistical Institute, vol. 88(2), pages 326-329, August.
  46. Alexandre C. Siqueira & Wolfgang Kiessling & David R. Bellwood, 2022. "Fast-growing species shape the evolution of reef corals," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
  47. You Wu & Jeremy Gaskins & Maiying Kong & Susmita Datta, 2018. "Profiling the effects of short time†course cold ischemia on tumor protein phosphorylation using a Bayesian approach," Biometrics, The International Biometric Society, vol. 74(1), pages 331-341, March.
  48. Jursa, Lukáš & Janků, Jan, 2025. "From the core to the European periphery: Spillover effects of financial cycles," Emerging Markets Review, Elsevier, vol. 68(C).
  49. Jing Zhou & Anirban Bhattacharya & Amy H. Herring & David B. Dunson, 2015. "Bayesian Factorizations of Big Sparse Tensors," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1562-1576, December.
  50. Martin Feldkircher & Florian Huber & Gary Koop & Michael Pfarrhofer, 2022. "APPROXIMATE BAYESIAN INFERENCE AND FORECASTING IN HUGE‐DIMENSIONAL MULTICOUNTRY VARs," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(4), pages 1625-1658, November.
  51. Argyro Damoulaki & Ioannis Ntzoufras & Konstantinos Pelechrinis, 2025. "Lasso multinomial performance indicators for in-play basketball data," Computational Statistics, Springer, vol. 40(4), pages 2157-2181, April.
  52. Park, Jaewoo & Jin, Ick Hoon & Schweinberger, Michael, 2022. "Bayesian model selection for high-dimensional Ising models, with applications to educational data," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).
  53. Jan van den Brakel & Martijn Souren & Sabine Krieg, 2022. "Estimating monthly labour force figures during the COVID‐19 pandemic in the Netherlands," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1560-1583, October.
  54. Hauzenberger, Niko & Huber, Florian & Klieber, Karin, 2023. "Real-time inflation forecasting using non-linear dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 39(2), pages 901-921.
  55. Niko Hauzenberger & Florian Huber & Luca Onorante, 2021. "Combining shrinkage and sparsity in conjugate vector autoregressive models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(3), pages 304-327, April.
  56. Rachael Meager, 2015. "Understanding the Impact of Microcredit Expansions: A Bayesian Hierarchical Analysis of 7 Randomised Experiments," Papers 1506.06669, arXiv.org, revised Jul 2016.
  57. Chan, Joshua C.C., 2021. "Minnesota-type adaptive hierarchical priors for large Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1212-1226.
  58. Korobilis, Dimitris & Landau, Bettina & Musso, Alberto & Phella, Anthoulla, 2021. "The time-varying evolution of inflation risks," Working Paper Series 2600, European Central Bank.
  59. Hamura, Yasuyuki & Irie, Kaoru & Sugasawa, Shonosuke, 2022. "Log-regularly varying scale mixture of normals for robust regression," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
  60. Andrew J. Womack & Luis León-Novelo & George Casella, 2014. "Inference From Intrinsic Bayes' Procedures Under Model Selection and Uncertainty," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1040-1053, September.
  61. Zikun Qin & Malay Ghosh, 2024. "Global-Local Shrinkage Priors for Asymptotic Point and Interval Estimation of Normal Means under Sparsity," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(1), pages 93-137, February.
  62. Jiayi Luo & Cindy Long Yu, 2021. "Determining Number of Factors in Dynamic Factor Models Contributing to GDP Nowcasting," Mathematics, MDPI, vol. 9(22), pages 1-23, November.
  63. Sun, Peng & Kim, Inyoung & Lee, Ki-Ahm, 2018. "Dual-semiparametric regression using weighted Dirichlet process mixture," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 162-181.
  64. Stefano Rizzelli, 2025. "Empirical Bayes methods in high dimensions: a survey and ongoing debates," METRON, Springer;Sapienza Università di Roma, vol. 83(3), pages 313-331, December.
  65. Manfred M. Fischer & Niko Hauzenberger & Florian Huber & Michael Pfarrhofer, 2021. "General Bayesian time-varying parameter VARs for predicting government bond yields," Papers 2102.13393, arXiv.org.
  66. Xueying Tang & Xiaofan Xu & Malay Ghosh & Prasenjit Ghosh, 2018. "Bayesian Variable Selection and Estimation Based on Global-Local Shrinkage Priors," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(2), pages 215-246, August.
  67. Michele Costola & Matteo Iacopini & Casper Wichers, 2023. "Bayesian SAR model with stochastic volatility and multiple time-varying weights," Papers 2310.17473, arXiv.org.
  68. 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).
  69. Banerjee, Sayantan, 2022. "Horseshoe shrinkage methods for Bayesian fusion estimation," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
  70. Miguel C. Herculano, 2026. "Bayesian Parametric Portfolio Policies," Papers 2602.21173, arXiv.org.
  71. Florian Huber & Michael Pfarrhofer, 2021. "Dynamic shrinkage in time‐varying parameter stochastic volatility in mean models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(2), pages 262-270, March.
  72. Maximilian Boeck & Massimiliano Marcellino & Michael Pfarrhofer & Tommaso Tornese, 2024. "Predicting Tail-Risks for the Italian Economy," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 20(3), pages 339-366, November.
  73. Li, Yunfan & Datta, Jyotishka & Craig, Bruce A. & Bhadra, Anindya, 2021. "Joint mean–covariance estimation via the horseshoe," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
  74. Yu Bai & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Macroeconomic forecasting in a multi‐country context," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1230-1255, September.
  75. Lucas D. Konrad & Lukas Vashold & Jesus Crespo Cuaresma, 2026. "Bayesian Indicator-Saturated Regression for Climate Policy Evaluation," Papers 2603.04997, arXiv.org.
  76. Maruyama, Yuzo & Matsuda, Takeru, 2025. "Minimaxity under the half-Cauchy prior," Journal of Multivariate Analysis, Elsevier, vol. 208(C).
  77. Gregor Kastner & Florian Huber, 2020. "Sparse Bayesian vector autoregressions in huge dimensions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1142-1165, November.
  78. Follett, Lendie & Yu, Cindy, 2019. "Achieving parsimony in Bayesian vector autoregressions with the horseshoe prior," Econometrics and Statistics, Elsevier, vol. 11(C), pages 130-144.
  79. Tamal Ghosh & Malay Ghosh & Jerry J. Maples & Xueying Tang, 2022. "Multivariate Global-Local Priors for Small Area Estimation," Stats, MDPI, vol. 5(3), pages 1-16, July.
  80. Zhang, Ruoyang & Ghosh, Malay, 2022. "Ultra high-dimensional multivariate posterior contraction rate under shrinkage priors," Journal of Multivariate Analysis, Elsevier, vol. 187(C).
  81. repec:rim:rimwps:20-09 is not listed on IDEAS
  82. Adam N. Smith & Jim E. Griffin, 2023. "Shrinkage priors for high-dimensional demand estimation," Quantitative Marketing and Economics (QME), Springer, vol. 21(1), pages 95-146, March.
  83. Paul Labonne & Leif Anders Thorsrud, 2023. "Risky news and credit market sentiment," Working Papers No 14/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  84. Kalli, Maria & Griffin, Jim E., 2014. "Time-varying sparsity in dynamic regression models," Journal of Econometrics, Elsevier, vol. 178(2), pages 779-793.
  85. Christopher Tosh & Mauricio Tec & Jessica B. White & Jeffrey F. Quinn & Glorymar Ibanez Sanchez & Paul Calder & Andrew L. Kung & Filemon S. Dela Cruz & Wesley Tansey, 2025. "A Bayesian active learning platform for scalable combination drug screens," Nature Communications, Nature, vol. 16(1), pages 1-18, December.
  86. Smith, Michael Stanley, 2023. "Implicit Copulas: An Overview," Econometrics and Statistics, Elsevier, vol. 28(C), pages 81-104.
  87. Ryan Martin & Bo Ning, 2020. "Empirical Priors and Coverage of Posterior Credible Sets in a Sparse Normal Mean Model," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(2), pages 477-498, August.
  88. Hauzenberger, Niko & Pfarrhofer, Michael & Rossini, Luca, 2025. "Sparse time-varying parameter VECMs with an application to modeling electricity prices," International Journal of Forecasting, Elsevier, vol. 41(1), pages 361-376.
  89. Martin Guth, 2022. "Predicting Default Probabilities for Stress Tests: A Comparison of Models," Papers 2202.03110, arXiv.org.
  90. Kaito Shimamura & Shuichi Kawano, 2021. "Bayesian sparse convex clustering via global-local shrinkage priors," Computational Statistics, Springer, vol. 36(4), pages 2671-2699, December.
  91. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2021. "Economic Predictions With Big Data: The Illusion of Sparsity," Econometrica, Econometric Society, vol. 89(5), pages 2409-2437, September.
  92. Kohns, David & Bhattacharjee, Arnab, 2023. "Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1384-1412.
  93. Dimitris Korobilis, 2025. "Exploring Monetary Policy Shocks with Large-Scale Bayesian VARs," Papers 2505.06649, arXiv.org.
  94. Daniel Spencer & Rajarshi Guhaniyogi & Raquel Prado, 2020. "Joint Bayesian Estimation of Voxel Activation and Inter-regional Connectivity in fMRI Experiments," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 845-869, December.
  95. Boonstra, Philip S. & Barbaro, Ryan P. & Sen, Ananda, 2019. "Default priors for the intercept parameter in logistic regressions," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 245-256.
  96. Joshua C. C. Chan, 2024. "BVARs and stochastic volatility," Chapters, in: Michael P. Clements & Ana Beatriz Galvão (ed.), Handbook of Research Methods and Applications in Macroeconomic Forecasting, chapter 3, pages 43-67, Edward Elgar Publishing.
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  98. Hannaford, Naomi E. & Heaps, Sarah E. & Nye, Tom M.W. & Curtis, Thomas P. & Allen, Ben & Golightly, Andrew & Wilkinson, Darren J., 2023. "A sparse Bayesian hierarchical vector autoregressive model for microbial dynamics in a wastewater treatment plant," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
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  103. Sayantan Paul & Arijit Chakrabarti, 2025. "Posterior contraction rate and asymptotic Bayes optimality for one group global–local shrinkage priors in sparse normal means problem," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 77(5), pages 787-819, October.
  104. Rong Tang & Yun Yang, 2022. "Bayesian inference for risk minimization via exponentially tilted empirical likelihood," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1257-1286, September.
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  109. Lee, Kyoungjae & Jo, Seongil & Lee, Jaeyong, 2022. "The beta-mixture shrinkage prior for sparse covariances with near-minimax posterior convergence rate," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
  110. Ouyang, Jiangrong & Bondell, Howard, 2023. "Bayesian analysis of longitudinal data via empirical likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
  111. Pedro A. Lima & Carlos M. Carvalho & Hedibert F. Lopes & Andrew Herren, 2025. "Minnesota BART," Papers 2503.13759, arXiv.org.
  112. Manfred M. Fischer & Niko Hauzenberger & Florian Huber & Michael Pfarrhofer, 2023. "General Bayesian time‐varying parameter vector autoregressions for modeling government bond yields," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 69-87, January.
  113. Valentina Mameli & Debora Slanzi & Jim E. Griffin & Philip J. Brown, 2025. "Multivariate Bayesian Global–Local Shrinkage Methods for Regularisation in the High-Dimensional Linear Model," Mathematics, MDPI, vol. 13(11), pages 1-21, May.
  114. 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.
  115. Sifat, Imtiaz & Zarei, Alireza & Hosseini, Seyedmehdi & Bouri, Elie, 2022. "Interbank liquidity risk transmission to large emerging markets in crisis periods," International Review of Financial Analysis, Elsevier, vol. 82(C).
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  117. Paponpat Taveeapiradeecharoen & Popkarn Arwatchanakarn, 2025. "Forecasting Thai inflation from univariate Bayesian regression perspective," Papers 2505.05334, arXiv.org, revised May 2025.
  118. Se Yoon Lee & Bowen Lei & Bani Mallick, 2020. "Estimation of COVID-19 spread curves integrating global data and borrowing information," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-17, July.
  119. Marco Molinari & Andrea Cremaschi & Maria De Iorio & Nishi Chaturvedi & Alun D. Hughes & Therese Tillin, 2022. "Bayesian nonparametric modelling of multiple graphs with an application to ethnic metabolic differences," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1181-1204, November.
  120. Jieun Lee & Gyuhyeong Goh, 2024. "A hybrid deterministic–deterministic approach for high-dimensional Bayesian variable selection with a default prior," Computational Statistics, Springer, vol. 39(3), pages 1659-1681, May.
  121. Uddin, Md Nazir & Gaskins, Jeremy T., 2023. "Shared Bayesian variable shrinkage in multinomial logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
  122. Onizuka, Takahiro & Iwashige, Fumiya & Hashimoto, Shintaro, 2024. "Bayesian boundary trend filtering," Computational Statistics & Data Analysis, Elsevier, vol. 191(C).
  123. Adämmer, Philipp & Prüser, Jan & Schüssler, Rainer A., 2025. "Forecasting macroeconomic tail risk in real time: Do textual data add value?," International Journal of Forecasting, Elsevier, vol. 41(1), pages 307-320.
  124. Korobilis, Dimitris, 2022. "A new algorithm for structural restrictions in Bayesian vector autoregressions," European Economic Review, Elsevier, vol. 148(C).
  125. Bettina Grün & Paul Hofmarcher, 2021. "Identifying groups of determinants in Bayesian model averaging using Dirichlet process clustering," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(3), pages 1018-1045, September.
  126. Lin Zhang & Veerabhadran Baladandayuthapani & Hongxiao Zhu & Keith A. Baggerly & Tadeusz Majewski & Bogdan A. Czerniak & Jeffrey S. Morris, 2016. "Functional CAR Models for Large Spatially Correlated Functional Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 772-786, April.
  127. Cássio Roberto de Andrade Alves & Márcio Laurini, 2023. "Estimating the Capital Asset Pricing Model with Many Instruments: A Bayesian Shrinkage Approach," Mathematics, MDPI, vol. 11(17), pages 1-20, September.
  128. Nicholas G. Polson & James G. Scott, 2016. "Mixtures, envelopes and hierarchical duality," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(4), pages 701-727, September.
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