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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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Quantile-Quantile plots under random censorship
Journal of Statistical Planning and Inference, 1986We obtain strong approximation results for the product-limit quantile- quantile process. In addition, product-limit confidence bands for the theoretical Q-Q plot are constructed.
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Estimating densities, quantiles, quantile densities and density quantiles
Annals of the Institute of Statistical Mathematics, 1992zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Expectiles and M-quantiles are quantiles
Statistics & Probability Letters, 1994zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Self-Calibrating Quantile–Quantile Plots
The American Statistician, 2016Quantile–quantile plots, or qqplots, are an important visual tool for many applications but their interpretation requires some care and often more experience. This apparent subjectivity is unnecessary. By drawing on the computational and display facilities now widely available, qqplots are easily enriched to help with their interpretation.
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A Quantile–Quantile Toolbox for Reference Intervals
The Journal of Applied Laboratory MedicineAbstractBackgroundParametric statistical methods are generally better than nonparametric, but require that data follow a known, usually normal, distribution. One important application is finding reference limits and detection limits. Parametric analyses yield better estimates and measures of their uncertainty than nonparametric approaches, which rely ...
Douglas M Hawkins, Rianne N Esquivel
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