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Predictions in Quantile Regressions
Open Journal of Statistics, 2014 Two different tools to evaluate quantile regression forecasts are proposed: MAD, to summarize forecast errors, and a fluctuation test to evaluate in-sample predictions. The scores of the PISA test to evaluate students’ proficiency are considered. Growth analysis relates school attainment to economic growth.
Marilena Furno
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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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Journal of Multivariate Analysis, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Adrover, Jorge +2 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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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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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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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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Regression Quantile and Averaged Regression Quantile Processes
2017We consider the averaged version \(\widetilde{B}_n(\alpha )\) of the two-step regression \(\alpha \)-quantile, introduced in [6] and studied in [7]. We show that it is asymptotically equivalent to the averaged version \(\bar{B}_n(\alpha )\) of ordinary regression quantile and also study the finite-sample relation of \(\widetilde{B}_n(\alpha )\) to ...
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