Results 301 to 310 of about 315,996 (341)
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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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Journal of Econometrics, 2019
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Machado, José A. F. +1 more
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Machado, José A. F. +1 more
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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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Estimating Quantile Sensitivities
Operations Research, 2009Quantiles of a random performance serve as important alternatives to the usual expected value. They are used in the financial industry as measures of risk and in the service industry as measures of service quality. To manage the quantile of a performance, we need to know how changes in the input parameters affect the output quantiles, which are called
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