Non‐crossing non‐parametric estimates of quantile curves
By Holger Dette, Stanislav Volgushev. 2008
159 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/1a45d0e0135d0a882b2047e1b5ce85e463b202b4
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In Artificial Intelligence
- Thomas Kneib
- Matt Taddy
- Torsten Hothorn
- Athanasios Kottas
- Peter Buhlmann
- Andrew D. Ho
- Jan Peters
- Marloes H. Maathuis
In Statistics and Probability
- Thomas Kneib
- Matt Taddy
- Torsten Hothorn
- Peter Buhlmann
- Athanasios Kottas
- Katherine Castellano
- Andrew D. Ho
- Jungmo Yoon
Citations in the corpus
- A Bayesian Nonparametric Approach to Inference for Quantile Regression (2010, methodology): “Estimated crossing quantiles may arise under classical nonparametric methods for quantile regression, and the related literature includes various techniques for addressing this problem (see, e.g., Dette and Volgushev 2008 and references therein).”
- Nonparametric estimation and inference on conditional quantile processes (2015, background): “, testing H0: Q( j1; z) = Q( j0; z) for all 2 T against H1: Q( j1; z) 6= Q( j0; z) for (1)This concept traces back to Lehmann (1975) and Doksum (1974); recent works include Heckman, Smith and Clements (1997), Abadie, Angrist and Imbens (2002), Chernozhukov and Hansen (2005) and Firpo (2007).”
- Conditional transformation models (2012, methodology): “Solutions to this problem can be obtained by combining all quantile fits in one joint model based on, for example, location-scale models (He 1997) or quantile sheets (Schnabel and Eilers 2012), or by monotonising the estimated quantile curves using non-decreasing rearrangements (Dette and Volgushev ”
- Beyond mean regression (2013, background): “…example based on simultaneous estimation of all quantiles in quantile sheets (Schnabel and Eilers, 2013a) or based on non-decreasing rearrangements (Dette and Volgushev, 2008) have been proposed in the literature but these always require additional efforts and are not always applicable with…”
- Contrasting OLS and Quantile Regression Approaches to Student “Growth” Percentiles (2013, background): “Crossed surfaces are a known complication of nonparametric quantile regression, where the theoretical monotonicity of conditional quantiles over t does not hold (Dette & Volgushev, 2008; Koenker, 2005).”