PMID- 36246856
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20221019
IS  - 0266-4763 (Print)
IS  - 1360-0532 (Electronic)
IS  - 0266-4763 (Linking)
VI  - 49
IP  - 14
DP  - 2022
TI  - Shrinkage estimation of fixed and random effects in linear quantile mixed models.
PG  - 3693-3716
LID - 10.1080/02664763.2021.1962262 [doi]
AB  - This paper presents a Bayesian analysis of linear mixed models for quantile 
      regression using a modified Cholesky decomposition for the covariance matrix of 
      random effects and an asymmetric Laplace distribution for the error distribution. 
      We consider several novel Bayesian shrinkage approaches for both fixed and random 
      effects in a linear mixed quantile model using extended L1 penalties. To improve 
      mixing of the Markov chains, a simple and efficient partially collapsed Gibbs 
      sampling algorithm is developed for posterior inference. We also extend the 
      framework to a Bayesian mixed expectile model and develop a Metropolis-Hastings 
      acceptance-rejection (MHAR) algorithm using proposal densities based on 
      iteratively weighted least squares estimation. The proposed approach is then 
      illustrated via both simulated and real data examples. Results indicate that the 
      proposed approach performs very well in comparison to the other approaches.
CI  - (c) 2021 Informa UK Limited, trading as Taylor & Francis Group.
FAU - Ji, Yonggang
AU  - Ji Y
AUID- ORCID: 0000-0002-5912-8231
AD  - School of Science, Civil Aviation University of China, Tianjin, People's Republic 
      of China.
FAU - Shi, Haifang
AU  - Shi H
AUID- ORCID: 0000-0001-9486-9572
AD  - School of Science, Civil Aviation University of China, Tianjin, People's Republic 
      of China.
LA  - eng
PT  - Journal Article
DEP - 20210806
PL  - England
TA  - J Appl Stat
JT  - Journal of applied statistics
JID - 9883455
PMC - PMC9559065
OTO - NOTNLM
OT  - Cholesky decomposition
OT  - Metropolis-Hastings acceptance-rejection
OT  - Quantile mixed regression
OT  - expectile mixed regression
OT  - partially collapsed Gibbs sampling
COIS- No potential conflict of interest was reported by the author(s).
EDAT- 2021/08/06 00:00
MHDA- 2021/08/06 00:01
PMCR- 2022/08/06
CRDT- 2022/10/17 04:39
PHST- 2022/10/17 04:39 [entrez]
PHST- 2021/08/06 00:00 [pubmed]
PHST- 2021/08/06 00:01 [medline]
PHST- 2022/08/06 00:00 [pmc-release]
AID - 1962262 [pii]
AID - 10.1080/02664763.2021.1962262 [doi]
PST - epublish
SO  - J Appl Stat. 2021 Aug 6;49(14):3693-3716. doi: 10.1080/02664763.2021.1962262. 
      eCollection 2022.