Nonparametric Estimation of an Additive Quantile Regression Model

By Joel L. Horowitz, S. Lee. 2004

191 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/fe9a4155ec90abeb34701d84098e7254b66fdf3f

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Citations in the corpus

VARIABLE SELECTION IN NONPARAMETRIC ADDITIVE MODELS (2010, background): “Therefore, by (12) and the triangle inequality,”
Local quantile regression (2010, background): “ine of literature considers structural semipara2 metric models to cope with the curse of dimensionality, like, partial linear models, Hardle et al. (2012), etc., additive models, Kong et al. (2010), Horowitz and Lee (2005), etc; single index models, Wu et al. (2010), Koenker (2010), etc. Yet another”
A Bayesian Nonparametric Approach to Inference for Quantile Regression (2010, methodology): “In Section 5.1 we develop the extension to nonparametric Tobit quantile regression, and in Section 5.2 we give a data example that illustrates this extension.”
Conditional quantile processes based on series or many regressors (2011, methodology): “We also build on Lee [28] that studied QR estimation of partially linear models in the series framework for a single quantile index, and on Horowitz and Lee [23] that studied nonparametric QR estimation of additive quantile models for a single quantile index in a series framework.”
Massachusetts Institute of Technology Department of Economics Working Paper Series Conditional Quantile Processes Based on Series or Many Regressors Conditional Quantile Processes Based on Series or Many Regressors (unknown): “We also build on Lee [28] that studied QR estimation of partially linear models in the series framework for a single quantile index, and on Horowitz and Lee [23] that studied nonparametric QR estimation of additive quantile models for a single quantile index in a series framework.”
Group Lasso for high dimensional sparse quantile regression models (2011, background): “In fact, they are adapted from Horowitz and Lee (2005).”
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.”
Model Selection via Bayesian Information Criterion for Quantile Regression Models (2014, methodology): “The conditions (i) and (iii) of (B2) are not strong and are satisfied in most nonparametric estimation problems based on spline basis approximation (see, e.g., Kim (2007) and Horowitz and Lee (2005)).”
Backfitting and smooth backfitting for additive quantile models (2010, background): “Horowitz and Lee (2005) studied an extension of the two-stage procedure of Horowitz and Mammen (2004) to quantile regression.”
UNIFORM BAHADUR REPRESENTATION FOR LOCAL POLYNOMIAL ESTIMATES OF M-REGRESSION AND ITS APPLICATION TO THE ADDITIVE MODEL (2007, unknown): “Previous work on additive quantile regression, for example, includes Linton (2001) and Horowitz and Lee (2005) for the i.i.d. case.”