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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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Journal of Economic Perspectives, 2001 Quantile regression, as introduced by Koenker and Bassett (1978), may be viewed as an extension of classical least squares estimation of conditional mean models to the estimation of an ensemble of models for several conditional quantile functions. The central special case is the median regression estimator which minimizes a sum of absolute errors ...
Koenker, Roger, Hallock, Kevin F.
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Quantile cointegrating regression [PDF]
Journal of Econometrics, 2009 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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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Linear Quantile Mixed Models: The lqmm Package for Laplace Quantile Regression
Journal of Statistical Software, 2014 Inference in quantile analysis has received considerable attention in the recent years. Linear quantile mixed models (Geraci and Bottai 2014) represent a ?exible statistical tool to analyze data from sampling designs such as multilevel, spatial, panel or
Marco Geraci
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Partially linear censored quantile regression [PDF]
, 2009 Censored regression quantile (CRQ) methods provide a powerful and flexible approach to the analysis of censored survival data when standard linear models are felt to be appropriate.
B Honore +17 more
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Simulation Study The Using of Bayesian Quantile Regression in Nonnormal Error
Cauchy: Jurnal Matematika Murni dan Aplikasi, 2018 The purposes of this paper is to introduce the ability of the Bayesian quantile regression method in overcoming the problem of the nonnormal errors using asymmetric laplace distribution on simulation study.
Catrin Muharisa +2 more
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Local linear spatial quantile regression [PDF]
, 2009 Copyright @ 2009 International Statistical Institute / Bernoulli Society for Mathematical Statistics and Probability.Let {(Yi,Xi), i ∈ ZN} be a stationary real-valued (d + 1)-dimensional spatial processes.
Hallin, M, Lu, Z, Yu, K
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Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks [PDF]
, 2009 Quantile regression is an increasingly important empirical tool in economics and other sciences for analyzing the impact of a set of regressors on the conditional distribution of an outcome. Extremal quantile regression, or quantile regression applied to
Chernozhukov, Victor +1 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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