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The Bayesian Lasso

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Cited by:

  1. Zou, Changliang & Chen, Xin, 2012. "On the consistency of coordinate-independent sparse estimation with BIC," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 248-255.
  2. Lun Li & Yan Long & Libin Zhang & Jessica Dalton-Morgan & Jacqueline Batley & Longjiang Yu & Jinling Meng & Maoteng Li, 2015. "Genome Wide Analysis of Flowering Time Trait in Multiple Environments via High-Throughput Genotyping Technique in Brassica napus L," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-18, March.
  3. Chinco, Alex & Neuhierl, Andreas & Weber, Michael, 2021. "Estimating the anomaly base rate," Journal of Financial Economics, Elsevier, vol. 140(1), pages 101-126.
  4. Einmahl, J.H.J. & Magnus, J.R. & Kumar, K., 2011. "On the Choice of Prior in Bayesian Model Averaging," Discussion Paper 2011-003, Tilburg University, Center for Economic Research.
  5. Minerva Mukhopadhyay & Tapas Samanta, 2017. "A mixture of g-priors for variable selection when the number of regressors grows with the sample size," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 377-404, June.
  6. Pfarrhofer, Michael, 2019. "Measuring international uncertainty using global vector autoregressions with drifting parameters," Working Papers in Economics 2019-3, University of Salzburg.
  7. Miguel A.G. Belmonte & Gary Koop & Dimitris Korobilis, 2014. "Hierarchical Shrinkage in Time‐Varying Parameter Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(1), pages 80-94, January.
  8. 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.
  9. Korobilis, Dimitris, 2015. "Quantile forecasts of inflation under model uncertainty," 2007 Annual Meeting, July 29-August 1, 2007, Portland, Oregon TN 2015-72, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  10. 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).
  11. 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.
  12. 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.
  13. Chen, Haiqiang & Li, Yingxing & Lin, Ming & Zhu, Yanli, 2018. "A Regime Shift Model with Nonparametric Switching Mechanism," IRTG 1792 Discussion Papers 2018-020, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  14. 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.
  15. Anne Musson & Damien Rousselière, 2020. "Exploring the effect of crisis on cooperatives: a Bayesian performance analysis of French craftsmen cooperatives," Applied Economics, Taylor & Francis Journals, vol. 52(25), pages 2657-2678, May.
  16. 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.
  17. Geraldine Ang & Dirk Röttgers & Pralhad Burli, 2017. "The empirics of enabling investment and innovation in renewable energy," OECD Environment Working Papers 123, OECD Publishing.
  18. Christophe Schalck & Meryem Yankol-Schalck, 2021. "Predicting French SME failures: new evidence from machine learning techniques," Applied Economics, Taylor & Francis Journals, vol. 53(51), pages 5948-5963, November.
  19. Koop, Gary & Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Bayesian compressed vector autoregressions," Journal of Econometrics, Elsevier, vol. 210(1), pages 135-154.
  20. 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.
  21. Saverio Ranciati & Giuliano Galimberti & Gabriele Soffritti, 2019. "Bayesian variable selection in linear regression models with non-normal errors," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(2), pages 323-358, June.
  22. 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.
  23. Yu-Zhu Tian & Man-Lai Tang & Wai-Sum Chan & Mao-Zai Tian, 2021. "Bayesian bridge-randomized penalized quantile regression for ordinal longitudinal data, with application to firm’s bond ratings," Computational Statistics, Springer, vol. 36(2), pages 1289-1319, June.
  24. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2016. "Testing for Granger causality in large mixed-frequency VARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 418-432.
  25. Dengluan Dai & Anmin Tang & Jinli Ye, 2023. "High-Dimensional Variable Selection for Quantile Regression Based on Variational Bayesian Method," Mathematics, MDPI, vol. 11(10), pages 1-22, May.
  26. Billio Monica & Casarin Roberto & Costola Michele & Iacopini Matteo, 2021. "COVID-19 spreading in financial networks: A semiparametric matrix regression model," Papers 2101.00422, arXiv.org.
  27. David Kohns & Tibor Szendrei, 2021. "Decoupling Shrinkage and Selection for the Bayesian Quantile Regression," Papers 2107.08498, arXiv.org.
  28. Kiranmoy Das & Bhuvanesh Pareek & Sarah Brown & Pulak Ghosh, 2022. "A semi-parametric Bayesian dynamic hurdle model with an application to the health and retirement study," Computational Statistics, Springer, vol. 37(2), pages 837-863, April.
  29. Walder, Adam & Hanks, Ephraim M., 2020. "Bayesian analysis of spatial generalized linear mixed models with Laplace moving average random fields," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
  30. Eliaz, Kfir & Spiegler, Ran, 2022. "On incentive-compatible estimators," Games and Economic Behavior, Elsevier, vol. 132(C), pages 204-220.
  31. Aijun Yang & Xuejun Jiang & Lianjie Shu & Jinguan Lin, 2017. "Bayesian variable selection with sparse and correlation priors for high-dimensional data analysis," Computational Statistics, Springer, vol. 32(1), pages 127-143, March.
  32. Scutari Marco & Balding David & Mackay Ian, 2013. "Improving the efficiency of genomic selection," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(4), pages 517-527, August.
  33. Jue Tao Lim & Yiting Han & Borame Sue Lee Dickens & Lee Ching Ng & Alex R Cook, 2020. "Time varying methods to infer extremes in dengue transmission dynamics," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-19, October.
  34. Prüser, Jan, 2017. "Forecasting US inflation using Markov dimension switching," Ruhr Economic Papers 710, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
  35. Ferri-García, Ramón & Castro-Martín, Luis & Rueda, María del Mar, 2021. "Evaluating Machine Learning methods for estimation in online surveys with superpopulation modeling," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 186(C), pages 19-28.
  36. 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.
  37. Compare, M. & Baraldi, P. & Bani, I. & Zio, E. & McDonnell, D., 2020. "Industrial equipment reliability estimation: A Bayesian Weibull regression model with covariate selection," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
  38. 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.
  39. 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.
  40. Gary Koop, 2012. "Using VARs and TVP-VARs with Many Macroeconomic Variables," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 4(3), pages 143-167, September.
  41. Elin Shaddox & Francesco C. Stingo & Christine B. Peterson & Sean Jacobson & Charmion Cruickshank-Quinn & Katerina Kechris & Russell Bowler & Marina Vannucci, 2018. "A Bayesian Approach for Learning Gene Networks Underlying Disease Severity in COPD," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(1), pages 59-85, April.
  42. Simon Beyeler, 2019. "Streamlining Time-varying VAR with a Factor Structure in the Parameters," Working Papers 19.03, Swiss National Bank, Study Center Gerzensee.
  43. 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).
  44. Damien Rousselière, 2019. "A Flexible Approach to Age Dependence in Organizational Mortality: Comparing the Life Duration for Cooperative and Non-Cooperative Enterprises Using a Bayesian Generalized Additive Discrete Time Survi," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(4), pages 829-855, December.
  45. 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.
  46. 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.
  47. Yinghan Chen & Steven Andrew Culpepper & Yuguo Chen, 2023. "Bayesian Inference for an Unknown Number of Attributes in Restricted Latent Class Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 613-635, June.
  48. Hwang, Youngdeok & Kim, Hang J. & Chang, Won & Yeo, Kyongmin & Kim, Yongku, 2019. "Bayesian pollution source identification via an inverse physics model," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 76-92.
  49. Bo Zhu & Miao Zhu & Jicai Jiang & Hong Niu & Yanhui Wang & Yang Wu & Lingyang Xu & Yan Chen & Lupei Zhang & Xue Gao & Huijiang Gao & Jianfeng Liu & Junya Li, 2016. "The Impact of Variable Degrees of Freedom and Scale Parameters in Bayesian Methods for Genomic Prediction in Chinese Simmental Beef Cattle," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-16, May.
  50. Luciano Rogério Braatz de Andrade & Massaine Bandeira e Sousa & Eder Jorge Oliveira & Marcos Deon Vilela de Resende & Camila Ferreira Azevedo, 2019. "Cassava yield traits predicted by genomic selection methods," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-22, November.
  51. Armagan, Artin & Dunson, David, 2011. "Sparse variational analysis of linear mixed models for large data sets," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 1056-1062, August.
  52. M. Marsman & K. Huth & L. J. Waldorp & I. Ntzoufras, 2022. "Objective Bayesian Edge Screening and Structure Selection for Ising Networks," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 47-82, March.
  53. Nicolai Meinshausen & Peter Bühlmann, 2010. "Stability selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 417-473, September.
  54. Chi Wang & Giovanni Parmigiani & Francesca Dominici, 2012. "Bayesian Effect Estimation Accounting for Adjustment Uncertainty," Biometrics, The International Biometric Society, vol. 68(3), pages 661-671, September.
  55. Jianhong Wang & Xiaoyan Lin, 2020. "A Bayesian approach for semiparametric regression analysis of panel count data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(2), pages 402-420, April.
  56. Fadel Hamid Hadi ALHUSSEINI, 2017. "Selection Of Variables Influencing Iraqi Banks Deposits By Using New Bayesian Lasso Quantile Regression," Journal of Social and Economic Statistics, Bucharest University of Economic Studies, vol. 6(1), pages 46-59, JULY.
  57. Weibing Li & Thierry Chekouo, 2022. "Bayesian group selection with non-local priors," Computational Statistics, Springer, vol. 37(1), pages 287-302, March.
  58. Lee, Kyu Ha & Chakraborty, Sounak & Sun, Jianguo, 2017. "Variable selection for high-dimensional genomic data with censored outcomes using group lasso prior," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 1-13.
  59. Roccazzella, Francesco & Gambetti, Paolo & Vrins, Frédéric, 2022. "Optimal and robust combination of forecasts via constrained optimization and shrinkage," International Journal of Forecasting, Elsevier, vol. 38(1), pages 97-116.
  60. Feihan Lu & Yao Zheng & Harrington Cleveland & Chris Burton & David Madigan, 2018. "Bayesian hierarchical vector autoregressive models for patient-level predictive modeling," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-27, December.
  61. Wang, Hong & Forbes, Catherine S. & Fenech, Jean-Pierre & Vaz, John, 2020. "The determinants of bank loan recovery rates in good times and bad – New evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 177(C), pages 875-897.
  62. 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).
  63. Mogliani, Matteo & Simoni, Anna, 2021. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Journal of Econometrics, Elsevier, vol. 222(1), pages 833-860.
  64. Fan, Jianqing & Jiang, Bai & Sun, Qiang, 2022. "Bayesian factor-adjusted sparse regression," Journal of Econometrics, Elsevier, vol. 230(1), pages 3-19.
  65. Billio, Monica & Casarin, Roberto & Rossini, Luca, 2019. "Bayesian nonparametric sparse VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 97-115.
  66. Kalli, Maria & Griffin, Jim E., 2014. "Time-varying sparsity in dynamic regression models," Journal of Econometrics, Elsevier, vol. 178(2), pages 779-793.
  67. David Kaplan, 2021. "On the Quantification of Model Uncertainty: A Bayesian Perspective," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 215-238, March.
  68. Manisha Sanjay Sirsat & Paula Rodrigues Oblessuc & Ricardo S. Ramiro, 2022. "Genomic Prediction of Wheat Grain Yield Using Machine Learning," Agriculture, MDPI, vol. 12(9), pages 1-12, September.
  69. Kaito Shimamura & Shuichi Kawano, 2021. "Bayesian sparse convex clustering via global-local shrinkage priors," Computational Statistics, Springer, vol. 36(4), pages 2671-2699, December.
  70. Cécile Grenier & Tuong-Vi Cao & Yolima Ospina & Constanza Quintero & Marc Henri Châtel & Joe Tohme & Brigitte Courtois & Nourollah Ahmadi, 2015. "Accuracy of Genomic Selection in a Rice Synthetic Population Developed for Recurrent Selection Breeding," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-25, August.
  71. 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.
  72. Junhao Pan & Edward Haksing Ip & Laurette Dubé, 2020. "Multilevel Heterogeneous Factor Analysis and Application to Ecological Momentary Assessment," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 75-100, March.
  73. G. Yi & J. Q. Shi & T. Choi, 2011. "Penalized Gaussian Process Regression and Classification for High-Dimensional Nonlinear Data," Biometrics, The International Biometric Society, vol. 67(4), pages 1285-1294, December.
  74. Sakae Oya, 2021. "A Bayesian Graphical Approach for Large-Scale Portfolio Management with Fewer Historical Data," Papers 2103.05880, arXiv.org, revised Mar 2022.
  75. De Luca, Giuseppe & Magnus, Jan R. & Peracchi, Franco, 2018. "Weighted-average least squares estimation of generalized linear models," Journal of Econometrics, Elsevier, vol. 204(1), pages 1-17.
  76. Iregui, Ana María & Núñez, Héctor M. & Otero, Jesús, 2021. "Testing the efficiency of inflation and exchange rate forecast revisions in a changing economic environment," Journal of Economic Behavior & Organization, Elsevier, vol. 187(C), pages 290-314.
  77. 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).
  78. Artin Armagan & Russell Zaretzki, 2010. "Model selection via adaptive shrinkage with t priors," Computational Statistics, Springer, vol. 25(3), pages 441-461, September.
  79. Trevor J. Hefley & Mevin B. Hooten & Ephraim M. Hanks & Robin E. Russell & Daniel P. Walsh, 2017. "The Bayesian Group Lasso for Confounded Spatial Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(1), pages 42-59, March.
  80. 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.
  81. Stefan Lang & Nikolaus Umlauf & Peter Wechselberger & Kenneth Harttgen & Thomas Kneib, 2012. "Multilevel structured additive regression," Working Papers 2012-07, Faculty of Economics and Statistics, Universität Innsbruck.
  82. Bernardi, Mauro & Bottone, Marco & Petrella, Lea, 2018. "Bayesian quantile regression using the skew exponential power distribution," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 92-111.
  83. 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.
  84. 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.
  85. Hashimoto, Elizabeth M. & Ortega, Edwin M.M. & Cancho, Vicente G. & Cordeiro, Gauss M., 2010. "The log-exponentiated Weibull regression model for interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1017-1035, April.
  86. Diógenes Ferreira Filho & Júlio Sílvio de Sousa Bueno Filho & Luciana Correia de Almeida Regitano & Maurício Mello de Alencar & Rosiana Rodrigues Alves & Sarah Laguna Conceição Meirelles, 2019. "Tournaments between markers as a strategy to enhance genomic predictions," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-17, June.
  87. Niko Hauzenberger, 2020. "Flexible Mixture Priors for Large Time-varying Parameter Models," Papers 2006.10088, arXiv.org, revised Nov 2020.
  88. Kastner, Gregor, 2019. "Sparse Bayesian time-varying covariance estimation in many dimensions," Journal of Econometrics, Elsevier, vol. 210(1), pages 98-115.
  89. Seongil Jo & Taeyoung Roh & Taeryon Choi, 2016. "Bayesian spectral analysis models for quantile regression with Dirichlet process mixtures," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(1), pages 177-206, March.
  90. Tian, Yuzhu & Song, Xinyuan, 2020. "Bayesian bridge-randomized penalized quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
  91. Lee Woojoo & Lee Donghwan & Lee Youngjo & Pawitan Yudi, 2011. "Sparse Canonical Covariance Analysis for High-throughput Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-24, July.
  92. Bhattacharya, Anirban & Dunson, David B. & Pati, Debdeep & Pillai, Natesh S., 2016. "Sub-optimality of some continuous shrinkage priors," Stochastic Processes and their Applications, Elsevier, vol. 126(12), pages 3828-3842.
  93. R. Alhamzawi & K. Yu & D. F. Benoit, 2011. "Bayesian adaptive Lasso quantile regression," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/728, Ghent University, Faculty of Economics and Business Administration.
  94. Yu Takagi & Hirokazu Matsuda & Yukio Taniguchi & Hiroaki Iwaisaki, 2014. "Predicting the Phenotypic Values of Physiological Traits Using SNP Genotype and Gene Expression Data in Mice," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-17, December.
  95. Sweata Sen & Damitri Kundu & Kiranmoy Das, 2023. "Variable selection for categorical response: a comparative study," Computational Statistics, Springer, vol. 38(2), pages 809-826, June.
  96. Oliver J. Rutz & Michael Trusov & Randolph E. Bucklin, 2011. "Modeling Indirect Effects of Paid Search Advertising: Which Keywords Lead to More Future Visits?," Marketing Science, INFORMS, vol. 30(4), pages 646-665, July.
  97. Anindya Bhadra & Jyotishka Datta & Nicholas G. Polson & Brandon T. Willard, 2020. "Global-Local Mixtures: A Unifying Framework," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(2), pages 426-447, August.
  98. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
  99. Madeleine St. Ville & Andrew W. Bergen & James W. Baurley & Joe D. Bible & Christopher S. McMahan, 2022. "Assessing Opioid Use Disorder Treatments in Trials Subject to Non-Adherence via a Functional Generalized Linear Mixed-Effects Model," IJERPH, MDPI, vol. 19(9), pages 1-21, April.
  100. Dufays, Arnaud & Rombouts, Jeroen V.K., 2020. "Relevant parameter changes in structural break models," Journal of Econometrics, Elsevier, vol. 217(1), pages 46-78.
  101. Foroutaifar Saheb, 2020. "Accuracy and sensitivity of different Bayesian methods for genomic prediction using simulation and real data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 19(3), pages 1-10, June.
  102. Bai, Jushan & Ando, Tomohiro, 2013. "Multifactor asset pricing with a large number of observable risk factors and unobservable common and group-specific factors," MPRA Paper 52785, University Library of Munich, Germany, revised Dec 2013.
  103. Ji, Yonggang & Lin, Nan & Zhang, Baoxue, 2012. "Model selection in binary and tobit quantile regression using the Gibbs sampler," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 827-839.
  104. Goh, Gyuhyeong & Dey, Dipak K. & Chen, Kun, 2017. "Bayesian sparse reduced rank multivariate regression," Journal of Multivariate Analysis, Elsevier, vol. 157(C), pages 14-28.
  105. Paolo Onorati & Brunero Liseo, 2022. "Bayesian Hierarchical Copula Models with a Dirichlet–Laplace Prior," Stats, MDPI, vol. 5(4), pages 1-17, November.
  106. 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.
  107. Goodness C. Aye & Rangan Gupta, 2013. "Forecasting Real House Price of the U.S.: An Analysis Covering 1890 to 2012," Working Papers 201362, University of Pretoria, Department of Economics.
  108. Roberto Casarin & Fausto Corradin & Francesco Ravazzolo & Nguyen Domenico Sartore & Wing-Keung Wong, 2020. "A Scoring Rule for Factor and Autoregressive Models Under Misspecification," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(2), pages 66-103, June.
  109. Schmich Fabian & Kuipers Jack & Merdes Gunter & Beerenwinkel Niko, 2019. "netprioR: a probabilistic model for integrative hit prioritisation of genetic screens," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(3), pages 1-16, June.
  110. Li, Hanning & Pati, Debdeep, 2017. "Variable selection using shrinkage priors," Computational Statistics & Data Analysis, Elsevier, vol. 107(C), pages 107-119.
  111. Jean-Pierre Dubé & Sanjog Misra, 2017. "Personalized Pricing and Consumer Welfare," NBER Working Papers 23775, National Bureau of Economic Research, Inc.
  112. Hu, Guanyu, 2021. "Spatially varying sparsity in dynamic regression models," Econometrics and Statistics, Elsevier, vol. 17(C), pages 23-34.
  113. Deborah Gefang & Geraint Johnes, 2017. "Asymmetric volatility spillovers between the U.K. regional worker flows and vacancies," Applied Economics, Taylor & Francis Journals, vol. 49(50), pages 5117-5133, October.
  114. Florian Huber & Tam'as Krisztin & Michael Pfarrhofer, 2018. "A Bayesian panel VAR model to analyze the impact of climate change on high-income economies," Papers 1804.01554, arXiv.org, revised Feb 2021.
  115. Ruggieri, Eric & Lawrence, Charles E., 2012. "On efficient calculations for Bayesian variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1319-1332.
  116. Okudo, Michiko & Komaki, Fumiyasu, 2021. "Shrinkage priors for single-spiked covariance models," Statistics & Probability Letters, Elsevier, vol. 176(C).
  117. Olivier Collignon & Jeongseop Han & Hyungmi An & Seungyoung Oh & Youngjo Lee, 2018. "Comparison of the modified unbounded penalty and the LASSO to select predictive genes of response to chemotherapy in breast cancer," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-15, October.
  118. Florian Huber & Gary Koop, 2023. "Fast and Order-invariant Inference in Bayesian VARs with Non-Parametric Shocks," Working Papers 2309, University of Strathclyde Business School, Department of Economics.
  119. Mauro Bernardi & Ghislaine Gayraud & Lea Petrella, 2013. "Bayesian inference for CoVaR," Papers 1306.2834, arXiv.org, revised Nov 2013.
  120. 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.
  121. Li, Jiahan & Chen, Weiye, 2014. "Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models," International Journal of Forecasting, Elsevier, vol. 30(4), pages 996-1015.
  122. Alhamzawi, Rahim, 2016. "Bayesian model selection in ordinal quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 68-78.
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