Results 211 to 220 of about 6,156 (248)
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Communication-Efficient Nonparametric Quantile Regression via Random Features
Journal of Computational And Graphical StatisticsThis article introduces a refined algorithm designed for distributed nonparametric quantile regression in a reproducing kernel Hilbert space (RKHS).
Caixing Wang +4 more
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Local asymptotics for nonparametric quantile regression with regression splines
Statistics & Probability Letters, 2016zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhao, Weihua, Lian, Heng
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2024 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)
As the share of distributed photovoltaic power generation increases rapidly, accurate and reliable regional photovoltaic power uncertainty quantifying becomes crucial to the economic and secure operation of power systems.
Zhiqiang He +5 more
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As the share of distributed photovoltaic power generation increases rapidly, accurate and reliable regional photovoltaic power uncertainty quantifying becomes crucial to the economic and secure operation of power systems.
Zhiqiang He +5 more
semanticscholar +1 more source
Bayesian Nonparametric Quantile Process Regression
2021Codes to reproduce QUINN, a novel Bayesian nonparametric quantile process regression.
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Periodicals of Engineering and Natural Sciences (PEN), 2020
This paper study two stratified quantile regression models of the marginal and the conditional varieties. We estimate the quantile functions of these models by using two nonparametric methods of smoothing spline (B-spline) and kernel regression (Nadaraya-
M. Ibrahim, Q. N. N. Al-Qazaz
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This paper study two stratified quantile regression models of the marginal and the conditional varieties. We estimate the quantile functions of these models by using two nonparametric methods of smoothing spline (B-spline) and kernel regression (Nadaraya-
M. Ibrahim, Q. N. N. Al-Qazaz
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Nonparametric Quantile Regression‐Based Classifiers for Bankruptcy Forecasting
Journal of Forecasting, 2013AbstractAn improved classification device for bankruptcy forecasting is proposed. The proposed approach relies on mainstream classifiers whose inputs are obtained from a so‐called multinorm analysis, instead of traditional indicators such as the ROA ratio and other accounting ratios. A battery of industry norms (computed by using nonparametric quantile
Lorca, Pedro +2 more
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Linear and Nonparametric Quantile Regression
2012Quantile regression estimates can be presented in tables alongside linear regression estimates. A possible advantage of this approach to presenting quantile regression results is that it is easy to compare the values of the coefficients and standard errors with OLS estimates and across quantiles. As we have seen, quantile estimates actually contain far
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Nonparametric M -quantile regression using penalised splines
Journal of Nonparametric Statistics, 2009Quantile regression investigates the conditional quantile functions of a response variable in terms of a set of covariates. M-quantile regression extends this idea by a ‘quantile-like’ generalisation of regression based on influence functions. In this work, we extend it to nonparametric regression, in the sense that the M-quantile regression functions ...
PRATESI, MONICA +2 more
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Nonparametric Multivariate Conditional Distribution and Quantile Regression
SSRN Electronic Journal, 2008In nonparametric multivariate regression analysis, one usually seeks methods to reduce the dimensionality of the regression function to bypass the difficulty caused by the curse of dimensionality. We study nonparametric estimation of multivariate conditional distribution and quantile regression via local univariate quadratic estimation of partial ...
Keming Yu +2 more
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Imputation in nonparametric quantile regression with complex data
Statistics & Probability Letters, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hu, Yanan +3 more
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