{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T12:41:16Z","timestamp":1763988076070,"version":"3.37.3"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T00:00:00Z","timestamp":1592179200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T00:00:00Z","timestamp":1592179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11631003","11690012"],"award-info":[{"award-number":["11631003","11690012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11701491","11726629"],"award-info":[{"award-number":["11701491","11726629"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Comput Stat"],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1007\/s00180-020-00998-w","type":"journal-article","created":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T08:02:52Z","timestamp":1592208172000},"page":"2033-2053","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Bayesian joint-quantile regression"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4276-3677","authenticated-orcid":false,"given":"Yingying","family":"Hu","sequence":"first","affiliation":[]},{"given":"Huixia Judy","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xuming","family":"He","sequence":"additional","affiliation":[]},{"given":"Jianhua","family":"Guo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,6,15]]},"reference":[{"key":"998_CR1","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1007\/s12561-016-9158-8","volume":"8","author":"R Alhamzawi","year":"2016","unstructured":"Alhamzawi R (2016) Bayesian analysis of composite quantile regression. Stat Biosci 8:358\u2013373","journal-title":"Stat Biosci"},{"issue":"2","key":"998_CR2","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/S0304-4076(03)00100-3","volume":"115","author":"V Chernozhukov","year":"2003","unstructured":"Chernozhukov V, Hong H (2003) An MCMC approach to classical estimation. J Econometrics 115(2):293\u2013346","journal-title":"J Econometrics"},{"key":"998_CR3","volume-title":"The new Palgrave dictionary of economics","author":"V Chernozhukov","year":"2008","unstructured":"Chernozhukov V, Du S (2008) Extremal quantiles and value-at-risk. In: Durlauf SN, Blume LE (eds) The new Palgrave dictionary of economics. Palgrave Macmillan, Basingstoke"},{"issue":"2","key":"998_CR4","doi-asserted-by":"crossref","first-page":"832","DOI":"10.3150\/13-BEJ589","volume":"21","author":"Y Feng","year":"2015","unstructured":"Feng Y, Chen Y, He X (2015) Bayesian quantile regression with approximate likelihood. Bernoulli 21(2):832\u2013850","journal-title":"Bernoulli"},{"key":"998_CR5","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/978-3-7908-2736-1_21","volume-title":"Recent advances in functional data analysis and related topics","author":"L Gardes","year":"2011","unstructured":"Gardes L, Girard S (2011) Functional kernel estimators of conditional extreme quantiles. In: Ferraty F (ed) Recent advances in functional data analysis and related topics. Springer, Berlin, pp 135\u2013140"},{"key":"998_CR6","first-page":"599","volume-title":"In: Bernardo JM, Berger JO, Dawid AP and Smith AMF (eds) Bayesian statistics 5","author":"A Gelman","year":"1996","unstructured":"Gelman A, Roberts GO, Gilks WR (1996) Efficient Metropolis jumping rules. In: Bernardo JM, Berger JO, Dawid AP and Smith AMF (eds) Bayesian statistics 5. Oxford Univ. Press, New York, pp 599\u2013607"},{"issue":"1","key":"998_CR7","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1093\/biostatistics\/kxj039","volume":"8","author":"M Geraci","year":"2007","unstructured":"Geraci M, Bottai M (2007) Quantile regression for longitudinal data using the asymmetric laplace distribution. Biostatistics 8(1):140\u2013154","journal-title":"Biostatistics"},{"issue":"4","key":"998_CR8","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1093\/biomet\/88.4.1035","volume":"88","author":"P Green","year":"2001","unstructured":"Green P, Mira A (2001) Delayed rejection in reversible jump Metropolis\u2013Hastings. Biometrika 88(4):1035\u20131053","journal-title":"Biometrika"},{"issue":"3","key":"998_CR9","first-page":"1075","volume":"22","author":"J Guo","year":"2012","unstructured":"Guo J, Tian M, Zhu K (2012) New efficient and robust estimation in varying-coefficient models with heteroscedasticity. Stat Sin 22(3):1075\u20131101","journal-title":"Stat Sin"},{"issue":"3","key":"998_CR10","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1007\/s001800050022","volume":"14","author":"H Haario","year":"1999","unstructured":"Haario H, Saksman E, Tamminen J (1999) Adaptive proposal distribution for random walk metropolis algorithm. Comput Stat 14(3):375\u2013395","journal-title":"Comput Stat"},{"issue":"2","key":"998_CR11","doi-asserted-by":"crossref","first-page":"223","DOI":"10.2307\/3318737","volume":"7","author":"H Haario","year":"2001","unstructured":"Haario H, Saksman E, Tamminen J (2001) An adaptive metropolis algorithm. Bernoulli 7(2):223\u2013242","journal-title":"Bernoulli"},{"issue":"4","key":"998_CR12","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1007\/s11222-006-9438-0","volume":"16","author":"H Haario","year":"2006","unstructured":"Haario H, Laine M, Mira A, Saksman E (2006) DRAM: efficient adaptive MCMC. Stat Comput 16(4):339\u2013354","journal-title":"Stat Comput"},{"issue":"18","key":"998_CR13","doi-asserted-by":"crossref","first-page":"3744","DOI":"10.1080\/00949655.2015.1014372","volume":"85","author":"HW Huang","year":"2015","unstructured":"Huang HW, Chen ZX (2015) Bayesian composite quantile regression. J Stat Comput Simul 85(18):3744\u20133754","journal-title":"J Stat Comput Simul"},{"issue":"4","key":"998_CR14","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1080\/10618600.2012.707454","volume":"22","author":"L Jiang","year":"2013","unstructured":"Jiang L, Wang H, Bondell HD (2013a) Interquantile shrinkage in regression models. J Comput Graph Stat 22(4):970\u2013986","journal-title":"J Comput Graph Stat"},{"key":"998_CR15","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.csda.2013.03.014","volume":"64","author":"R Jiang","year":"2013","unstructured":"Jiang R, Zhou Z, Qian W, Chen Y (2013b) Two-step composite quantile regression for single-index models. Comput Stat Data Anal 64:180\u2013191","journal-title":"Comput Stat Data Anal"},{"issue":"5","key":"998_CR16","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1080\/02331888.2018.1500579","volume":"52","author":"R Jiang","year":"2018","unstructured":"Jiang R, Hu XP, Yu KM, Qian WM (2018) Composite quantile regression for massive datasets. Statistics 52(5):980\u20131004","journal-title":"Statistics"},{"issue":"1","key":"998_CR17","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1111\/j.1467-9868.2009.00725.x","volume":"72","author":"B Kai","year":"2010","unstructured":"Kai B, Li R, Zou H (2010) Local composite quantile regression smoothing: an efficient and safe alternative to local polynomial regression. J R Stat Soc B 72(1):49\u201369","journal-title":"J R Stat Soc B"},{"key":"998_CR18","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.jmva.2012.05.004","volume":"112","author":"K Khare","year":"2012","unstructured":"Khare K, Hobert JP (2012) Geometric ergodicity of the Gibbs sampler for Bayesian quantile regression. J Multivar Anal 112:108\u2013116","journal-title":"J Multivar Anal"},{"issue":"6","key":"998_CR19","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/0167-7152(84)90040-3","volume":"2","author":"R Koenker","year":"1984","unstructured":"Koenker R (1984) A note on $L$-estimates for linear models. Stat Probab Lett 2(6):323\u2013325","journal-title":"Stat Probab Lett"},{"key":"998_CR20","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511754098","volume-title":"Quantile regression","author":"R Koenker","year":"2005","unstructured":"Koenker R (2005) Quantile regression. Cambridge University Press, Cambridge"},{"issue":"1","key":"998_CR21","doi-asserted-by":"crossref","first-page":"33","DOI":"10.2307\/1913643","volume":"46","author":"R Koenker","year":"1978","unstructured":"Koenker R, Bassett G (1978) Regression quantiles. Econometrica 46(1):33\u201350","journal-title":"Econometrica"},{"issue":"11","key":"998_CR22","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1080\/00949655.2010.496117","volume":"81","author":"H Kozumi","year":"2011","unstructured":"Kozumi H, Kobayashi G (2011) Gibbs sampling methods for Bayesian quantile regression. J Stat Comput Simul 81(11):1565\u20131578","journal-title":"J Stat Comput Simul"},{"key":"998_CR23","first-page":"533","volume":"5","author":"Q Li","year":"2010","unstructured":"Li Q, Xi R, Lin N (2010) Bayesian regularized quantile regression. Bayesian Anal 5:533\u2013556","journal-title":"Bayesian Anal"},{"issue":"3\u20134","key":"998_CR24","first-page":"231","volume":"59","author":"A Mira","year":"2002","unstructured":"Mira A (2002) On Metropolis\u2013Hastings algorithms with delayed rejection. Metron 59(3\u20134):231\u2013241","journal-title":"Metron"},{"key":"998_CR25","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1111\/biom.12053","volume":"69","author":"BJ Reich","year":"2013","unstructured":"Reich BJ, Smith LB (2013) Bayesian quantile regression for censored data. Biometrics 69:651\u2013660","journal-title":"Biometrics"},{"issue":"493","key":"998_CR26","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1198\/jasa.2010.ap09237","volume":"106","author":"BJ Reich","year":"2011","unstructured":"Reich BJ, Fuentes M, Dunson DB (2011) Bayesian spatial quantile regression. J Am Stat Assoc 106(493):6\u201320","journal-title":"J Am Stat Assoc"},{"issue":"2","key":"998_CR27","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1080\/10618600.2016.1172016","volume":"26","author":"T Rodrigues","year":"2017","unstructured":"Rodrigues T, Fan Y (2017) Regression adjustment for noncrossing Bayesian quantile regression. J Comput Graph Stat 26(2):275\u2013284","journal-title":"J Comput Graph Stat"},{"issue":"3","key":"998_CR28","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1080\/10618600.2019.1575225","volume":"28","author":"T Rodrigues","year":"2019","unstructured":"Rodrigues T, Dortet-Bernadet J-L, Fan Y (2019) Pyramid quantile regression. J Comput Graph Stat 28(3):732\u2013746","journal-title":"J Comput Graph Stat"},{"key":"998_CR29","volume-title":"Simultaneous Bayesian estimation of multiple quantiles with an extension to hierarchical models","author":"K Sriram","year":"2012","unstructured":"Sriram K, Ramamoorthi RV, Ghosh P (2012) Simultaneous Bayesian estimation of multiple quantiles with an extension to hierarchical models. Social Science Electronic Publishing, New York"},{"key":"998_CR30","first-page":"87","volume":"78\u2013A","author":"K Sriram","year":"2016","unstructured":"Sriram K, Ramamoorthi RV, Ghosh P (2016) On Bayesian quantile regression using a pseudo-joint asymmetric Laplace likelihood. Sankhy\u0101 Indian J Stat 78\u2013A:87\u2013104","journal-title":"Sankhy\u0101 Indian J Stat"},{"issue":"3","key":"998_CR31","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1111\/sjos.12307","volume":"45","author":"Y Tang","year":"2018","unstructured":"Tang Y, Wang H, Liang H (2018) Composite estimation for single-index models with responses subject to detection limits. Scand J Stat 45(3):444\u2013464","journal-title":"Scand J Stat"},{"issue":"15","key":"998_CR32","doi-asserted-by":"crossref","first-page":"7717","DOI":"10.1080\/03610926.2016.1161798","volume":"46","author":"YZ Tian","year":"2017","unstructured":"Tian YZ, Lian H, Tian MZ (2017) Bayesian composite quantile regression for linear mixed-effects models. Commun Stat Theory Methods 46(15):7717\u20137731","journal-title":"Commun Stat Theory Methods"},{"key":"998_CR33","doi-asserted-by":"crossref","first-page":"2961","DOI":"10.1256\/qj.04.176","volume":"131","author":"SM Uppala","year":"2005","unstructured":"Uppala SM, Kallberg PW et al (2005) The era-40 re-analysis. Q J R Meteorol Soc 131:2961\u20133012","journal-title":"Q J R Meteorol Soc"},{"key":"998_CR34","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.1080\/01621459.2013.820134","volume":"108","author":"H Wang","year":"2013","unstructured":"Wang H, Li D (2013) Estimation of extreme conditional quantiles through power trnsformation. J Am Stat Assoc 108:1062\u20131074","journal-title":"J Am Stat Assoc"},{"issue":"1","key":"998_CR35","first-page":"295","volume":"26","author":"K Wang","year":"2016","unstructured":"Wang K, Wang H (2016) Optimally combined estimation for tail quantile regression. Stat Sin 26(1):295\u2013311","journal-title":"Stat Sin"},{"key":"998_CR36","first-page":"41","volume-title":"Handbook of quantile regression, Chapman & Hall\/CRC handbooks of modern statistical methods","author":"H Wang","year":"2018","unstructured":"Wang H, Yang Y (2018) Bayesian quantile regression. In: Koenker R, Chernozhukov V, He X, Peng L (eds) Handbook of quantile regression, Chapman & Hall\/CRC handbooks of modern statistical methods. CRC Press, Boca Raton, pp 41\u201354"},{"key":"998_CR37","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1080\/01621459.2012.716382","volume":"107","author":"H Wang","year":"2012","unstructured":"Wang H, Li D, He X (2012) Estimation of high conditional quantiles for heavy-tailed distributions. J Am Stat Assoc 107:1453\u20131464","journal-title":"J Am Stat Assoc"},{"issue":"9","key":"998_CR38","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1080\/03610926.2019.1568493","volume":"49","author":"YK Wu","year":"2020","unstructured":"Wu YK, Tian MZ, Tang ML (2020) General composite quantile regression: theory and methods. Commun Stat Theory Methods 49(9):2217\u20132236","journal-title":"Commun Stat Theory Methods"},{"issue":"519","key":"998_CR39","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1080\/01621459.2016.1192545","volume":"112","author":"Y Yang","year":"2017","unstructured":"Yang Y, Tokdar S (2017) Joint estimation of quantile planes over arbitrary predictor spaces. J Am Stat Assoc 112(519):1107\u20131120","journal-title":"J Am Stat Assoc"},{"issue":"3","key":"998_CR40","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1111\/insr.12114","volume":"84","author":"Y Yang","year":"2016","unstructured":"Yang Y, Wang H, He X (2016) Posterior inference in Bayesian quantile regression with asymmetric Laplace likelihood. Int Stat Rev 84(3):327\u2013344","journal-title":"Int Stat Rev"},{"issue":"4","key":"998_CR41","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/S0167-7152(01)00124-9","volume":"54","author":"K Yu","year":"2001","unstructured":"Yu K, Moyeed RA (2001) Bayesian quantile regression. Stat Probab Lett 54(4):437\u2013447","journal-title":"Stat Probab Lett"},{"issue":"1","key":"998_CR42","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.jeconom.2005.10.002","volume":"137","author":"K Yu","year":"2007","unstructured":"Yu K, Stander J (2007) Bayesian analysis of a Tobit quantile regression model. J Econom 137(1):260\u2013276","journal-title":"J Econom"},{"issue":"6","key":"998_CR43","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.1017\/S0266466614000176","volume":"30","author":"Z Zhao","year":"2014","unstructured":"Zhao Z, Xiao Z (2014) Efficient regressions via optimally combining quantile information. Econom Theory 30(6):1272\u20131314","journal-title":"Econom Theory"},{"issue":"3","key":"998_CR44","first-page":"1108","volume":"36","author":"H Zou","year":"2008","unstructured":"Zou H, Yuan M (2008) Composite quantile regression and the oracle model selection theory. Ann Stat 36(3):1108\u20131126","journal-title":"Ann Stat"}],"container-title":["Computational Statistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00180-020-00998-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00180-020-00998-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00180-020-00998-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,13]],"date-time":"2021-07-13T18:17:23Z","timestamp":1626200243000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00180-020-00998-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,15]]},"references-count":44,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["998"],"URL":"https:\/\/doi.org\/10.1007\/s00180-020-00998-w","relation":{},"ISSN":["0943-4062","1613-9658"],"issn-type":[{"type":"print","value":"0943-4062"},{"type":"electronic","value":"1613-9658"}],"subject":[],"published":{"date-parts":[[2020,6,15]]},"assertion":[{"value":"30 May 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}