Results 221 to 230 of about 56,994 (264)
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Journal of Multivariate Analysis, 2022
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2014
A guide to the implementation and interpretation of Quantile Regression models. This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensive description of the main issues ...
Davino, Cristina +2 more
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A guide to the implementation and interpretation of Quantile Regression models. This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensive description of the main issues ...
Davino, Cristina +2 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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Journal of Statistical Planning and Inference, 2004
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Adrover, Jorge +2 more
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Adrover, Jorge +2 more
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On Extreme Regression Quantiles
Extremes, 1999\textit{R. Koenker} and \textit{G. Basset} [Econometrica 46, 33-50, (1978; Zbl 0373.62038)] introduced regression quantiles to generalize the notion of order statistic from the case of a single sample to the linear regression setting. In linear models regression quantiles estimate the conditional quantile of the response at each value of the ...
Portnoy, Stephen, Jurečková, Jana
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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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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.
Domenico Vistocco, Marilena Furno
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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.
Domenico Vistocco, Marilena Furno
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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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ARCH tests and quantile regressions
Journal of Statistical Computation and Simulation, 2004We consider a test based on quantile regressions to verify the presence of conditional heteroskedasticity. The test does not rely on distributional assumptions of the errors, nor on a function describing the pattern of heteroskedasticity. It compares the slope coefficients of the regressions computed at different quantiles.
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