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Causal K-Means Clustering. [PDF]
J R Stat Soc Series B Stat MethodolKim K, Kim J, Kennedy EH.
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Information Sciences, 2021
Nonparametric quantile regression is a commonly used nonlinear quantile model. One general and popular approach is based on the use of kernels within a reproducing kernel Hilbert space (RKHS) framework, with the smoothing splines estimation as a special ...
Fodé Zhang, Heng Lian
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Nonparametric quantile regression is a commonly used nonlinear quantile model. One general and popular approach is based on the use of kernels within a reproducing kernel Hilbert space (RKHS) framework, with the smoothing splines estimation as a special ...
Fodé Zhang, Heng Lian
exaly +2 more sources
Nonparametric quantile scalar-on-image regression
Computational Statistics & Data AnalysiszbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chuchu Wang, Xinyuan Song 0001
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Quantile regression: a nonparametric approach
Computational Statistics and Data Analysis, 1988Abstract Regression on any p -th quantile is considered through nonparametric modelling. The nonparametric technique used is moving parabolic fit which is known to be adaptative and to reduce bias in the usual mean regression. The quantile problem reduces to solving weighted linear regression in L 1 norm at each x -point and the iteratively ...
Pascal Sarda
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Investigating distribution of nitrate concentration using ensemble nonparametric quantile regression
Science of the Total Environment, 2021Nitrate ( NO3−) pollution in groundwater is a major concern due to its negative health effects; therefore, accurately estimating and predicting the NO3− concentration in groundwater is necessary.
Hojun You, Dugin Kaown, Eun-Hee Koh
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Convergence rate for nonparametric quantile regression with a total variation penalty
Stat, 2021Quantile regression with a total variation penalty was previously proposed due to its computational expediency as well as its local adaptiveness. However, the convergence rate of the method in this setting has been not rigorously established.
Jiamin Liu, Wangli Xu, Heng Lian
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A Bayesian Nonparametric Approach to Inference for Quantile Regression
Journal of Business and Economic Statistics, 2010We develop a Bayesian method for nonparametric model–based quantile regression. The approach involves flexible Dirichlet process mixture models for the joint distribution of the response and the covariates, with posterior inference for different quantile curves emerging from the conditional response distribution given the covariates.
Athanasios Kottas
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IEEE Transactions on Power Systems, 2023
Probabilistic forecasting that quantifies the prediction uncertainties is crucial for decision-making in power systems. As a prevalent nonparametric probabilistic forecasting approach, traditional machine learning-based quantile regression encounters the
Wenkang Cui, C. Wan, Yonghua Song
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Probabilistic forecasting that quantifies the prediction uncertainties is crucial for decision-making in power systems. As a prevalent nonparametric probabilistic forecasting approach, traditional machine learning-based quantile regression encounters the
Wenkang Cui, C. Wan, Yonghua Song
semanticscholar +1 more source
Eastern European Economics, 2023
This paper applies a novel time series-based additive nonparametric quantile regression technique to stress test the credit risk of conventional and participation banks in Turkey.
Resul Aydemir +2 more
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This paper applies a novel time series-based additive nonparametric quantile regression technique to stress test the credit risk of conventional and participation banks in Turkey.
Resul Aydemir +2 more
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