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Binary quantile regression: a Bayesian approach based on the asymmetric Laplace distribution

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Cited by:

  1. G. De Novellis & M. Doretti & G. E. Montanari & M. G. Ranalli & N. Salvati, 2024. "Performance evaluation of nursing homes using finite mixtures of logistic models and M-quantile regression for binary data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(3), pages 753-781, July.
  2. Chen, Le-Yu & Lee, Sokbae, 2018. "Best subset binary prediction," Journal of Econometrics, Elsevier, vol. 206(1), pages 39-56.
  3. Rahim Alhamzawi & Haithem Taha Mohammad Ali, 2020. "Brq: an R package for Bayesian quantile regression," METRON, Springer;Sapienza Università di Roma, vol. 78(3), pages 313-328, December.
  4. 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.
  5. Ramsey, A., 2018. "Conditional Distributions of Crop Yields: A Bayesian Approach for Characterizing Technological Change," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277253, International Association of Agricultural Economists.
  6. Florios, Kostas, 2018. "A hyperplanes intersection simulated annealing algorithm for maximum score estimation," Econometrics and Statistics, Elsevier, vol. 8(C), pages 37-55.
  7. A Ford Ramsey, 2020. "Probability Distributions of Crop Yields: A Bayesian Spatial Quantile Regression Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(1), pages 220-239, January.
  8. Yves S. Schüler, 2014. "Asymmetric Effects of Uncertainty over the Business Cycle: A Quantile Structural Vector Autoregressive Approach," Working Paper Series of the Department of Economics, University of Konstanz 2014-02, Department of Economics, University of Konstanz.
  9. Benoit, Dries F. & Van den Poel, Dirk, 2017. "bayesQR: A Bayesian Approach to Quantile Regression," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i07).
  10. Henry R. Scharf & Xinyi Lu & Perry J. Williams & Mevin B. Hooten, 2022. "Constructing Flexible, Identifiable and Interpretable Statistical Models for Binary Data," International Statistical Review, International Statistical Institute, vol. 90(2), pages 328-345, August.
  11. Chiuling Lu & Ann Yang & Jui-Feng Huang, 2015. "Bankruptcy predictions for U.S. air carrier operations: a study of financial data," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(3), pages 574-589, July.
  12. Peter Congdon, 2017. "Quantile regression for overdispersed count data: a hierarchical method," Journal of Statistical Distributions and Applications, Springer, vol. 4(1), pages 1-19, December.
  13. Jamal Bouoiyour & Refk Selmi, 2017. "The Bitcoin price formation: Beyond the fundamental sources," Papers 1707.01284, arXiv.org.
  14. V L Miguéis & D F Benoit & D Van den Poel, 2013. "Enhanced decision support in credit scoring using Bayesian binary quantile regression," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(9), pages 1374-1383, September.
  15. Yunwen Yang & Huixia Judy Wang & Xuming He, 2016. "Posterior Inference in Bayesian Quantile Regression with Asymmetric Laplace Likelihood," International Statistical Review, International Statistical Institute, vol. 84(3), pages 327-344, December.
  16. Oh, Man-Suk & Park, Eun Sug & So, Beong-Soo, 2016. "Bayesian variable selection in binary quantile regression," Statistics & Probability Letters, Elsevier, vol. 118(C), pages 177-181.
  17. Schüler, Yves S., 2020. "The impact of uncertainty and certainty shocks," Discussion Papers 14/2020, Deutsche Bundesbank.
  18. Magzhanov, Timur & Sagradyan, Anna, 2023. "Ambiguous high scores: The All-Russian Olympiad in economics during the COVID-19 pandemic," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 70, pages 89-108.
  19. Yukiko Omata & Hajime Katayama & Toshi. H. Arimura, 2017. "Same concerns, same responses? A Bayesian quantile regression analysis of the determinants for supporting nuclear power generation in Japan," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 19(3), pages 581-608, July.
  20. Karthik Sriram & R. V. Ramamoorthi & Pulak Ghosh, 2016. "On Bayesian Quantile Regression Using a Pseudo-joint Asymmetric Laplace Likelihood," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 78(1), pages 87-104, February.
  21. Victor Muthama Musau & Carlo Gaetan & Paolo Girardi, 2022. "Clustering of bivariate satellite time series: A quantile approach," Environmetrics, John Wiley & Sons, Ltd., vol. 33(7), November.
  22. Yuzhi Cai, 2018. "A novel statistical approach to marketing campaigns," Working Papers 2018-21, Swansea University, School of Management.
  23. B. Dima & Ş. M. Dima, 2016. "Income Distribution and Social Tolerance," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 128(1), pages 439-466, August.
  24. Dries Benoit & Rahim Alhamzawi & Keming Yu, 2013. "Bayesian lasso binary quantile regression," Computational Statistics, Springer, vol. 28(6), pages 2861-2873, December.
  25. Sriram, Karthik, 2015. "A sandwich likelihood correction for Bayesian quantile regression based on the misspecified asymmetric Laplace density," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 18-26.
  26. Bouoiyour, Jamal & Selmi, Refk & Miftah, Amal, 2015. "“Every cloud has a silver lining”; to what extent does the Arab Spring accelerate the integration among Arab monarchies?," MPRA Paper 70942, University Library of Munich, Germany.
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