Results 31 to 40 of about 3,032,172 (354)
High‐dimensional quantile regression: Convolution smoothing and concave regularization [PDF]
Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2021 ℓ1 ‐penalized quantile regression (QR) is widely used for analysing high‐dimensional data with heterogeneity. It is now recognized that the ℓ1 ‐penalty introduces non‐negligible estimation bias, while a proper use of concave regularization may lead to ...
Kean Ming Tan, Lan Wang, Wen-Xin Zhou
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Vector quantile regression [PDF]
, 2014 We propose a notion of conditional vector quantile function and a vector quantile regression. A conditional vector quantile function (CVQF) of a random vector Y, taking values in ℝd given covariates Z=z, taking values in ℝk, is a map u↦QY∣Z(u,z), which is monotone, in the sense of being a gradient of a convex function, and such that given that vector U
Guillaume Carlier +2 more
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Parametric Elliptical Regression Quantiles
Revstat Statistical Journal, 2020 The article extends linear and nonlinear quantile regression to the case of vector responses by generalizing multivariate elliptical quantiles to a regression context.
Daniel Hlubinka , Miroslav Šiman
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M-quantile regression analysis of temporal gene expression data [PDF]
, 2009 In this paper, we explore the use of M-regression and M-quantile coefficients to detect statistical differences between temporal curves that belong to different experimental conditions.
Vinciotti, V, Yu, K
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Nonparametric Multiple-Output Center-Outward Quantile Regression [PDF]
Journal of the American Statistical Association, 2022 Building on recent measure-transportation-based concepts of multivariate quantiles, we are considering the problem of nonparametric multiple-output quantile regression.
E. del Barrio +2 more
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Penalized flexible Bayesian quantile regression [PDF]
, 2012 Copyright © 2012 SciResThis article has been made available through the Brunel Open Access Publishing Fund.The selection of predictors plays a crucial role in building a multiple regression model. Indeed, the choice of a suitable subset of predictors can
Alhamzawi, R, Alkenani, A, Yu, K
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Fintech, financial inclusion and income inequality: a quantile regression approach
European Journal of Finance, 2020 Although theory suggests that financial market imperfections – mainly information asymmetries, market segmentation and transaction costs – prevent poor people from escaping poverty by limiting their access to formal financial services, new financial ...
Ayşegül Demir +3 more
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Smoothed instrumental variables quantile regression [PDF]
The Stata Journal, 2022 In this article, I introduce the sivqr command, which estimates the coefficients of the instrumental variables quantile regression model introduced by Chernozhukov and Hansen (2005, Econometrica 73: 245–261).
David M. Kaplan
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Choosing the Right Spatial Weighting Matrix in a Quantile Regression Model [PDF]
, 2013 This paper proposes computationally tractable methods for selecting the appropriate spatial weighting matrix in the context of a spatial quantile regression model.
Kostov, Phillip
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ERM Scheme for Quantile Regression
Abstract and Applied Analysis, 2013 This paper considers the ERM scheme for quantile regression. We conduct error analysis for this learning algorithm by means of a variance-expectation bound when a noise condition is satisfied for the underlying probability measure. The learning rates are
Dao-Hong Xiang
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