Bayesian quantile regression methods
By T. Lancaster, Sung Jae Jun. 2010
112 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/bdcbac4345dea37bab051ae783a4f8d0d098f381
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In Artificial Intelligence
- Huixia Judy Wang
- Anna Simoni
- Minchul Shin
- Yunwen Yang
- Xuming He
- Siddhartha Chib
- Richard Gerlach
- Heng Lian
In Statistics and Probability
- Huixia Judy Wang
- Yunwen Yang
- Anna Simoni
- Minchul Shin
- Xuming He
- Siddhartha Chib
- Richard Gerlach
- Heng Lian
Citations in the corpus
- Bayesian quantile regression for longitudinal data models (2012, methodology): “Lipsitz et al. [11] made use of Jung’s work and described the use of quantile regression methods to analyse longitudinal data on CD4 cell counts, and they followed the work of Robins et al. [12] to account for missing at random dropouts by weighting the generalized estimating equations.”
- Bayesian empirical likelihood for quantile regression (2012, methodology): “Schennach (2005) and Lancaster and Jun (2010) considered Bayesian exponentially tilted empirical likelihood (ETEL), which can be viewed as a nonparametric Bayesian procedure with noninformative priors on the space of distributions.”
- A generalized class of skew distributions and associated robust quantile regression models (2014, background): “See, among others, Horowitz & Lee (2009) and Lancaster & Jun (2010).”
- Bayesian Estimation and Comparison of Moment Condition Models (2016, methodology): “erjee (2006) and Chang and Mukerjee (2008) study the higher-order asymptotic and coverage properties of the Bayesian EL/ETEL posterior distribution for the population mean, while Schennach (2005) and Lancaster and Jun (2010) consider the large-sample behavior of the Bayesian ETEL posterior distribut”
- Simultaneous Linear Quantile Regression: A Semiparametric Bayesian Approach (2012, background): “Therefore, the issue of simultaneous inference remains unsolved (see, however, Dunson and Taylor 2005; Lancaster and Jun 2010, who offer partial solutions based on pseudo and empirical likelihoods).”
- Posterior Inference in Bayesian Quantile Regression with Asymmetric Laplace Likelihood (2016, background): “Lancaster & Jun (2010) considered exponential tilted empirical likelihood, while Otsu (2008) and Yang & He (2012) considered empirical likelihood.”