Quantile Regression in Reproducing Kernel Hilbert Spaces

By Youjuan Li, Yufeng Liu, Ji Zhu. 2007

240 citations recorded by the source corpus.

Subfields: Computational Mechanics, Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2df03455e55b0cf38f60ee9cca1276dbbea98b47

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Citations in the corpus

Conditional Density Estimation via Least-Squares Density Ratio Estimation (2010, methodology): “Kernel quantile regression (KQR) allows one to predict the 100τ -percentile of conditional distributions for a given τ (∈ (0, 1)) when y is one-dimensional (Takeuchi et al., 2006; Li et al., 2007).”
L1-Norm Quantile Regression (2008, methodology): “Meyer and Woodroofe (2000) discussed (3.”
VARIABLE SELECTION IN QUANTILE REGRESSION (2009, methodology): “Wang, Li and Jiang (2007) considered LADR with the adaptive LASSO penalty.”
Stepwise multiple quantile regression estimation using non-crossing constraints (2009, methodology): “To further improve the computation efficiency and facilitate the choice of the tuning parameter λ in (2), Li and Zhu (2008) and Li et al. (2007) developed entire solution paths of linear and kernel regularized quantile regression with respect to λ for any fixed τ ∈ (0, 1) respectively.”
Optimal Bandwidth Selection for Nonparametric Conditional Distribution and Quantile Functions (2013, background): “…Bassett (1978) appeared, quantile regression has exploded in popularity and a range of innovative approaches toward the estimation of conditional quantiles has been proposed including Yu and Jones (1998), Koenker and Xiao (2004), Li, Liu, and Zhu (2007), and Peng and Huang (2008) to name but a few.”
Mean Absolute Percentage Error for regression models (2016, methodology): “Then, the kernel quantile optimization problem, treated in [8, 9], is deﬁned by: min where λ > 0 handles the trade-oﬀ between the data ﬁtting term and the regularization term.”
Optimal regression rates for SVMs using Gaussian kernels (2013, methodology): “An approach similar to ours is used in [21] to estimate the distance of the SVM estimator to f τ,P.”
Conditional quantile analysis when covariates are functions, with application to growth data (2012, methodology): “Keywords: Conditional distribution function; Conditional reference chart; Functional data analysis; Growth data analysis; Quantile analysis; Uniform weak convergence”