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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
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Hodges—Lehmann quantile-quantile plots
Computational Statistics & Data Analysis, 1988zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Aly E.-E.A.A., Öztürk A.
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CONFIDENCE BANDS FOR QUANTILE-QUANTILE PLOTS
Statistics & Risk Modeling, 1986Summary: In this paper we rigorously obtain confidence bands for Q-Q plots via the strong approximation results of the first author [Strong approximations of the Q-Q process. Preprint (1983)] and \textit{M. Csörgö} and \textit{P. Révész} [Ann. Stat. 6, 882-894 (1978; Zbl 0378.62050)]. Our confidence bands are modified versions of \textit{M. Csörgö} and
Aly, Emad-Eldin A. A., Bleuer, Susana
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Biometrika, 1988
Summary: It is well known that an M-estimator of the centre of symmetry \(\theta\) of a symmetric distribution can be defined in terms of either a continuous symmetric loss function \(\rho\) or the associated influence function \(\psi\). This estimator is robust if \(\psi\) is bounded.
Breckling, Jens, Chambers, Ray
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Summary: It is well known that an M-estimator of the centre of symmetry \(\theta\) of a symmetric distribution can be defined in terms of either a continuous symmetric loss function \(\rho\) or the associated influence function \(\psi\). This estimator is robust if \(\psi\) is bounded.
Breckling, Jens, Chambers, Ray
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Quantile Regression on Quantile Ranges
SSRN Electronic Journal, 2010Motivated by the fact that a linear specification in a quantile regression setting is unable to describe the non-linear relations among economic variables, well documented in the empirical econometrics literature, we formulate a threshold quantile regression model for one, known and unknown threshold value.
Chung-Ming Kuan +2 more
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Frequency Connectedness and Cross-quantile Dependence Between Green Bond and Green Equity Markets
Energy Economics, 2020This paper aims at investigating the frequency connectedness and cross-quantile dependence between green bond and green equity markets. By decomposing green bond and green equity time series data into different frequency bands, we first identify how the ...
L. Pham
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