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Reduced varying coefficient models for regional quantile regression with multiple responses. [PDF]
BiometricsJung W, Park S, Hong HG, Lee ER.
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Modelling the Proportions with Excessive Endpoints Based on a Generalized Lindley Binomial Model. [PDF]
J Stat Theory ApplDeng D, Zhang X.
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Computationally efficient whole-genome quantile regression at biobank scale. [PDF]
Proc Natl Acad Sci U S AWang F +7 more
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On Distribution and Quantile Functions, Ranks and Signs in R_d
, 2017 Marc Hallin
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Quantile Transfer for Reliable Operating Point Selection in Visual Place Recognition [PDF]
Dhyey Manish Rajani +2 more
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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
openaire +1 more source