Additive models for quantile regression: Model selection and confidence bandaids

By Roger W. Koenker. 2010

144 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/18e7c0e8978026d000e17e3767368c2cc45f228d

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In Statistics and Probability

Citations in the corpus

A UNIFIED VARIABLE SELECTION APPROACH FOR VARYING COEFFICIENT MODELS (2012, methodology): “The data is based on the Demographic and Health Surveys data from 2005–2006, available free of charge for research purposes at http://www.measuredhs.com/ countries/start.cfm. Fenske, Kneib, and Hothorn (2009), and Koenker (2010) analyzed the whole data set by fitting nonparametric 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.”
Nonparametric estimation and inference on conditional quantile processes (2015, methodology): “Parametric approach to conditional quantile modelling is pioneered by Koenker and Bassett (1978), while the nonparametric approach is developed in a series of papers including Stone (1977), Chaudhuri (1991), Fan, Hu and Truong (1994), Koenker, Portnoy and Ng (1994) and Koenker (2010).”
Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models (2013, methodology): “We will assess statistical significance of socio-economic and biological factors on children’s malnutrition, providing a methodological follow up on the previous studies done by Fenske, Kneib, and Hothorn (2011) and Koenker (2011).”
A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification (2021, background): “Active work continues today to analyze the statistical properties of quantile regression and its variants under diﬀerent conditions, for example in additive models [97] or to improve conditional coverage when the size of the intervals may correlate with miscoverage events [12].”
Local quantile regression (2010, background): “Yet another strand of literature has been involved in ultra-high dimensional situations where a careful variable selection technique needs to be implemented, Belloni and Chernozhukov (2010) and Koenker (2010).”