The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Gibbs sampling methods for Bayesian quantile regression
Open accessAbstract
This paper considers quantile regression models using an asymmetric Laplace distribution from a Bayesian point of view. We develop a simple and efficient Gibbs sampling algorithm for fitting the quantile regression model based on a location-scale mixture representation of the asymmetric Laplace distribution. It is shown that the resulting Gibbs sampler can be accomplished by sampling from either normal or generalized inverse Gaussian distribution. We also discuss some possible extensions of our approach, including the incorporation of a scale parameter, the use of double exponential prior, and a Bayesian analysis of Tobit quantile regression. The proposed methods are illustrated by both simulated and real data.
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Cites
Regression shrinkage and selection via the lasso
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Practical markov chain monte carlo
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Non-Gaussian Ornstein–Uhlenbeck-based Models and Some of Their Uses in Financial Economics
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Robust Tests for Heteroscedasticity Based on Regression Quantiles
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Metadata
- Authors
- Kozumi, H., & Kobayashi, G.
- Venue
- Journal of Statistical Computation and Simulation
- Publisher
- Informa UK Limited
- Published
- 2011-03-17
- Volume
- 81
- Issue
- 11
- Pages
- 1565-1578
- ISSN
- 0094-9655 (ISSN-L)
- 1563-5163
- OpenAlex
- W1998073888
- Cited by
- 498
- References
- 33
- FWCI
- 18.08
- Type
- journal article
- Language
- en
- Open access
- green
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- Kozumi, H., & Kobayashi, G. (2011). Gibbs sampling methods for Bayesian quantile regression. Journal of Statistical Computation and Simulation. https://doi.org/10.1080/00949655.2010.496117
Data sources
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
