Spatial Quantile Multiple Regression Using the Asymmetric Laplace Process

By Kristian Lum, Alan E. Gelfand. 2012

115 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/bf799d20cd75fea6fed415c5f78a678b921d9958

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. Mauro Bernardi
5. Matteo Bottai
6. Ghislaine Gayraud
7. Marco Geraci
8. Lea Petrella

In Economics and Econometrics

1. Mauro Bernardi
2. Yunwen Yang
3. Matteo Bottai
4. Huixia Judy Wang
5. Ghislaine Gayraud
6. Marco Geraci
7. Xuming He
8. Lea Petrella

In Environmental Engineering

1. Yunwen Yang
2. Matteo Bottai
3. Huixia Judy Wang
4. Marco Geraci
5. Xuming He

Citations in the corpus

• Bayesian Tail Risk Interdependence Using Quantile Regression (2015, background): “For a detailed review and references, see for example, Lum and Gelfand (2012) and Koenker (2005).”
• Linear quantile mixed models (2013, methodology): “As explained by Lum and Gelfand (2012), the stronger constraint of a joint quantile specification is replaced by the weaker stochastic ordering of the asymmetric Laplace.”
• Posterior Inference in Bayesian Quantile Regression with Asymmetric Laplace Likelihood (2016, methodology): “…variable selection for quantile regression (Li et al., 2010; Alhamzawi et al., 2012; Alhamzawi & Yu, 2013; 2012), spatial quantile regression (Lum & Gelfand, 2012), quantile regression for count data with application to environmental epidemiology (Lee & Neocleous, 2010), non-parametric and…”