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.
Mei Ling Huang +2 more
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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.
Joel L Horowitz, Sokbae Lee
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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, 2019 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 insight into the influence of the functional covariate on different parts, like the center as well as ...
Chowdhury, Joydeep, Chaudhuri, Probal
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Nonparametric Multiple-Output Center-Outward Quantile Regression [PDF]
Journal of the American Statistical Association, 2022 36 ...
Barrio Tellado, Eustasio del +2 more
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SPECIFICATION TESTING IN NONPARAMETRIC INSTRUMENTAL QUANTILE REGRESSION [PDF]
Econometric Theory, 2020 There are many environments in econometrics which require nonseparable modeling of a structural disturbance. In a nonseparable model with endogenous regressors, key conditions are validity of instrumental variables and monotonicity of the model in a scalar unobservable variable.
Christoph Breunig
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Nonparametric inference on smoothed quantile regression process
Computational Statistics & Data Analysis, 2023 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Meiling Hao +3 more
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Quantile processes for semi and nonparametric regression
Electronic Journal of Statistics, 2017 To Appear in Electronic Journal of ...
Shih-Kang Chao +2 more
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