2026-09-19T23:14:49Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/29522 2023-07-10 col_2142_5131 col_2142_17362 com_2142_5130 com_2142_17361 com_2142_8903

Bayesian inference provides a flexible way of combiningg data with prior information. However, quantile regression is not equipped with a parametric likelihood, and therefore, Bayesian inference for quantile regression demands careful investigations. This thesis considers the Bayesian empirical likelihood approach to quantile regression. Taking the empirical likelihood into a Bayesian framework, we show that the resultant posterior is asymptotically normal; its mean shrinks towards the true parameter values and its variance approaches that of the maximum empirical likelihood estimator. Through empirical likelihood, the proposed method enables us to explore various forms of commonality across quantiles for efficiency gains in the estimation of multiple quantiles. By using an MCMC algorithm in the computation, we avoid the daunting task of directly maximizing empirical likelihoods. The finite sample performance of the proposed method is investigated empirically, where substantial efficiency gains are demonstrated with informative priors on common features across quantile levels. He, Xuming He, Xuming Chen, Yuguo Koenker, Roger W. Portnoy, Stephen L. Yang, Yunwen 2012-02-01T00:53:52Z 2014-02-01T11:00:33Z 2011-12 2012-02-01T00:53:52Z 2011-12 Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2011-07-22T19:07:13Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Yang_Yunwen.pdf: 446652 bytes, checksum: 5d8410ecb4177c0ae139a42b11b7692e (MD5) Made available in DSpace on 2012-02-01T00:53:52Z (GMT). No. of bitstreams: 2 Yang_Yunwen.pdf: 445649 bytes, checksum: 34bdad10dbf94b88e65e17cdfdde7e40 (MD5) license.txt: 4059 bytes, checksum: 21198f47f7d9b6133ad20d9cab6dfceb (MD5) Item marked as restricted to the 'Administrator' Group (id=1) by William Ingram (wingram2@illinois.edu) on 2012-02-01T00:57:03Z Item is restricted until 2014-02-01T00:56:58Z Item reinstated by Sarah Shreeves (sshreeve@illinois.edu) on 2014-02-01T11:00:33Z Item was in collections: Graduate Theses and Dissertations at Illinois (ID: 204) Dissertations and Theses - Statistics (ID: 774) No. of bitstreams: 3 Yang_Yunwen.pdf.txt: 187379 bytes, checksum: 4c7c0019f8fdbf233754c358824f766b (MD5) Yang_Yunwen.pdf: 445649 bytes, checksum: 34bdad10dbf94b88e65e17cdfdde7e40 (MD5) license.txt: 4059 bytes, checksum: 21198f47f7d9b6133ad20d9cab6dfceb (MD5) Item released from any restrictions by Sarah Shreeves (sshreeve@illinois.edu) on 2014-02-01T11:00:33Z http://hdl.handle.net/2142/29522 en Copyright 2011 Yunwen Yang Efficiency Empirical likelihood High quantiles Quantile regression Prior Posterior. Bayesian empirical likelihood for quantile regression Statistics 1583 Statistics 0329 University of Illinois at Urbana-Champaign Dissertation Ph.D. PHD:Statistics -UIUC 10KS0329PHD