Linear quantile mixed models

By Marco Geraci, Matteo Bottai. Statistics and computing, 2013

313 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/af2e7ba7c4605c6c81bd706853540ceeb1fc42ba

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Huixia Judy Wang
2. Yunwen Yang
3. Xuming He
4. C. Bayes
5. Mário de Castro
6. Jorge L. Bazán
7. Ryan Martin
8. Nicholas Syring

In Statistics and Probability

1. Huixia Judy Wang
2. Mário de Castro
3. Yunwen Yang
4. Xuming He
5. C. Bayes
6. Jorge L. Bazán
7. Ying Wei
8. Gauss Moutinho Cordeiro

Citations in the corpus

• A quantile parametric mixed regression model for bounded response variables (2017, methodology): “This approach has been extended to a random intercept regression model by [16] and to a mixed regression model by [17].”
• Posterior Inference in Bayesian Quantile Regression with Asymmetric Laplace Likelihood (2016, background): “…regression in different contexts and applications, for instance, quantile regression with random effects (Geraci & Bottai, 2007; Yuan & Yin, 2009; Geraci & Bottai, 2013; Yue & Rue, 2011; Luo et al., 2014; Wang, 2012), variable selection for quantile regression (Li et al., 2010; Alhamzawi et al.,…”