Results 211 to 220 of about 2,329,699 (248)

Robust regression quantiles

Journal of Statistical Planning and Inference, 2004
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Adrover, Jorge, Maronna, Ricardo A., Yohai, Víctor J.   +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

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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Quantile Regression on Quantile Ranges

SSRN Electronic Journal, 2010
Motivated 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, Christos Michalopoulos, Zhijie Xiao   +2 more
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Nonstandard Quantile-Regression Inference

Econometric Theory, 2007
It 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 ...
Chuan Goh, Keith Knight
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ARCH tests and quantile regressions

Journal of Statistical Computation and Simulation, 2004
We 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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Regression Quantile and Averaged Regression Quantile Processes

2017
We 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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Estimating Equivalence with Quantile Regression

Ecological Applications, 2010
Equivalence testing and corresponding confidence interval estimates are used to provide more enlightened statistical statements about parameter estimates by relating them to intervals of effect sizes deemed to be of scientific or practical importance rather than just to an effect size of zero.
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Regression Quantiles

Econometrica, 1978
Koenker, Roger W, Bassett, Gilbert, Jr
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Regression Models for Quantiles

Journal of Mathematical Sciences, 2001
Blagoveshchenskii, Yu. N., Svyatodukh, I. V.   +1 more
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