Results 311 to 320 of about 3,032,172 (354)
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Wiley Series in Probability and Statistics, 2018
Volume two of Quantile Regression offers an important guide for applied researchers that draws on the same example-based approach adopted for the first volume. The text explores topics including robustness, expectiles, m-quantile, decomposition, time series, elemental sets and linear programming.
Furno Marilena, Vistocco Domenico
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Volume two of Quantile Regression offers an important guide for applied researchers that draws on the same example-based approach adopted for the first volume. The text explores topics including robustness, expectiles, m-quantile, decomposition, time series, elemental sets and linear programming.
Furno Marilena, Vistocco Domenico
semanticscholar +5 more sources
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
Statistica Sinica, 2020
Summary: The quantile regression method is a valuable complement to the classical mean regression, helping to ensure robust and comprehensive data analyses in a variety of applications. We propose a novel envelope quantile regression (EQR) method that adapts a nascent technique called enveloping to improve the efficiency of the standard quantile ...
Ding, Shanshan +3 more
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Summary: The quantile regression method is a valuable complement to the classical mean regression, helping to ensure robust and comprehensive data analyses in a variety of applications. We propose a novel envelope quantile regression (EQR) method that adapts a nascent technique called enveloping to improve the efficiency of the standard quantile ...
Ding, Shanshan +3 more
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On the Unit-Chen distribution with associated quantile regression and applications
Mathematica Slovaca, 2022In this paper, a new distribution defined on (0, 1) is introduced. It is obtained by the transformation of a positive random variable following the Chen distribution with respect to the inverted exponential function.
M. C. Korkmaz +3 more
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IEEE Transactions on Smart Grid, 2022
The increased penetration of Renewable Energy Sources (RES) as part of a decentralized and distributed power system makes net-load forecasting a critical component in the planning and operation of power systems.
A. Faustine, Lucas Pereira
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The increased penetration of Renewable Energy Sources (RES) as part of a decentralized and distributed power system makes net-load forecasting a critical component in the planning and operation of power systems.
A. Faustine, Lucas Pereira
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Journal of Statistical Planning and Inference, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Adrover, Jorge +2 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Adrover, Jorge +2 more
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Nonstandard Quantile-Regression Inference
SSRN Electronic Journal, 2005It is well known that conventional Wald-type inference in the context of quantile regression is complicated by the need to construct estimates of the conditional densities of the response variables at the quantile of interest. This note explores the possibility of circumventing the need to construct conditional density estimates in this context with ...
Goh, S. C., Knight, K.
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IEEE Transactions on Smart Grid, 2021
To support daily operation of smart grid, the stochastic load behavior is analyzed by a day-ahead prediction interval (PI) which is built from predictor’s probability density function, computed in statistical mean-variance, and achieves a symmetrical PI.
Happy Aprillia +2 more
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To support daily operation of smart grid, the stochastic load behavior is analyzed by a day-ahead prediction interval (PI) which is built from predictor’s probability density function, computed in statistical mean-variance, and achieves a symmetrical PI.
Happy Aprillia +2 more
semanticscholar +1 more source
FUNCTIONAL ADDITIVE QUANTILE REGRESSION
Statistica Sinica, 2021Summary: We investigate a functional additive quantile regression that models the conditional quantile of a scalar response based on the nonparametric effects of a functional predictor. We model the nonparametric effects of the principal component scores as additive components, which are approximated by B-splines.
Zhang, Yingying +3 more
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