Nonparametric C- and D-vine-based quantile regression [PDF]
Dependence Modeling, 2022 Quantile regression is a field with steadily growing importance in statistical modeling. It is a complementary method to linear regression, since computing a range of conditional quantile functions provides more accurate modeling of the stochastic ...
Tepegjozova Marija +3 more
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Nonparametric quantile regression captures regional variability and scaling deviations in Atlantic surfclam length–weight relationships [PDF]
Scientific ReportsThe universality of the allometric model for describing the length–weight relationship in marine species has been questioned, particularly for some invertebrates such as sea urchins, clams, and barnacles.
Gorka Bidegain +6 more
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An Algorithm of Nonparametric Quantile Regression
Journal of Statistical Theory and Practice, 2023 Extreme events, such as earthquakes, tsunamis, and market crashes, can have substantial impact on social and ecological systems. Quantile regression can be used for predicting these extreme events, making it an important problem that has applications in many fields. Estimating high conditional quantiles is a difficult problem.
William Marshall, Mei Ling Huang
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Nonparametric Estimation of an Additive Quantile Regression Model [PDF]
Journal of the American Statistical Association, 2005 This paper is concerned with estimating the additive components of a nonparametric additive quantile regression model. We develop an estimator that is asymptotically normally distributed with a rate of convergence in probability of n-r/(2r+1) when the additive components are r-times continuously differentiable for some r ≥ 2.
Sokbae, Joel Horowitz
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Expansion for moments of regression quantiles with applications to nonparametric testing [PDF]
Bernoulli, 2019 We discuss nonparametric tests for parametric specifications of regression quantiles. The test is based on the comparison of parametric and nonparametric fits of these quantiles. The nonparametric fit is a Nadaraya-Watson quantile smoothing estimator. An asymptotic treatment of the test statistic requires the development of new mathematical arguments ...
Enno Mammen +2 more
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Uniform in Bandwidth Consistency of the L1-Modal Regression Estimator for High-Dimensional Data [PDF]
EntropyWe propose a new nonparametric estimator of the conditional mode in a regression framework where the covariates are functional in nature. The estimator is constructed through a quantile regression approach, which provides a robust alternative to ...
Fatimah A. Almulhim +2 more
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Nonparametric Instrumental Variables Estimation of a Quantile Regression Model [PDF]
Econometrica, 2007 We consider nonparametric estimation of a regression function that is identified by requiring a specified quantile of the regression "error" conditional on an instrumental variable to be zero. The resulting estimating equation is a nonlinear integral equation of the first kind, which generates an ill-posed inverse problem.
Sokbae, Joel Horowitz
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Ten-year trends and influencing factors of hospitalization costs in chronic obstructive pulmonary disease: a quantile regression analysis (2016–2025) [PDF]
Frontiers in Health ServicesBackgroundTo investigate the composition, temporal trends, and factors associated with per-admission hospitalization costs for chronic obstructive pulmonary disease (COPD)—related admissions from 2016 to 2025, adjusted for inflation and temporal effects,
Jieyun Zhu +9 more
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Nonparametric Multiple-Output Center-Outward Quantile Regression [PDF]
Journal of the American Statistical Association36 ...
Marc Hallin, Eustasio del Barrio Tellado
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Nonparametric Smoothing for Extremal Quantile Regression with Heavy Tailed Data
Revstat Statistical Journal, 2021 In several different fields, it is interested in analyzing the upper or lower tail quantile of the underlying distribution rather than mean or center quantile.
Takuma Yoshida
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