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 ...
William Marshall +2 more
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Modeling Spatial Data with Heteroscedasticity Using PLVCSAR Model: A Bayesian Quantile Regression Approach [PDF]
EntropySpatial data not only enables smart cities to visualize, analyze, and interpret data related to location and space, but also helps departments make more informed decisions.
Rongshang Chen, Zhiyong Chen
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Nonparametric depth and quantile regression for functional data [PDF]
Bernoulli, 2016 We investigate nonparametric regression methods based on spatial depth and quantiles when the response and the covariate are both functions. As in classical quantile regression for finite dimensional data, regression techniques developed here provide ...
Joydeep Chowdhury, P. Chaudhuri
semanticscholar +6 more sources
Nonparametric Multiple-Output Center-Outward Quantile Regression [PDF]
Journal of the American Statistical Association, 2022 Building on recent measure-transportation-based concepts of multivariate quantiles, we are considering the problem of nonparametric multiple-output quantile regression.
Eustasio del Barrio +2 more
semanticscholar +5 more sources
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 Lee, Joel L Horowitz
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Nonparametric inference on smoothed quantile regression process
Computational Statistics & Data Analysis, 2022 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Meiling Hao +3 more
semanticscholar +4 more sources
Quantile processes for semi and nonparametric regression
Electronic Journal of Statistics, 2017 To Appear in Electronic Journal of ...
Guang Cheng, Stanislav Volgushev
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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
doaj +2 more sources