On Additive Conditional Quantiles With High-Dimensional Covariates

By J. De Gooijer, Dawit Zerom. 2003

92 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/444108baf7d4aae26eb8f3135eb90efeb9451a55

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. Zhijie Xiao
2. Mi-Ok Kim
3. Zongwu Cai
4. Shujie Ma
5. James W. Taylor
6. Xiaoping Xu
7. Xuming He
8. Jianhui Zhou

In Statistics and Probability

1. Zhijie Xiao
2. Mi-Ok Kim
3. James W. Taylor
4. Xiaoping Xu
5. Zongwu Cai
6. Xuming He
7. Shujie Ma
8. Ying Wei

Citations in the corpus

• Nonparametric Quantile Estimations for Dynamic Smooth Coefficient Models (2008, methodology): “To estimate each component, for the time series case, De Gooijer and Zerom (2003) first estimated a highdimensional quantile function by inverting the conditional distribution function estimated by using a weighted Nadaraya– Watson approach of Cai (2002) and then used a projection © 2008 American…”
• Semiparametric quantile regression estimation in dynamic models with partially varying coefficients (2012, background): “For example, He, Ng and Portnoy (1998), He and Ng (1999), He and Portnoy (2000), De Gooijer and Zerom (2003), Yu and Lu (2004), and Horowitz and Lee (2005) considered the additive quantile regression models for iid data, while Honda (2004) and Cai and Xu (2008) investigated the varying coefficient…”
• Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall (2008, background): “However, De Gooijer and Zerom (2003) suggest that in many practical situations a non-linear model is needed to capture the underlying structure in a quantile.”
• Quantile regression with varying coefficients (2007, background): “De Gooijer and Zerom [6] and Doksum and Koo [7] considered local polynomials and polynomial splines with additive models.”
• Inference for single-index quantile regression models with profile optimization (2016, background): “De Gooijer and Zerom (2003), Horowitz and Lee (2005) and Koenker (2011) proposed additive quantile regression models.”