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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
Yu, K, Alkenani, A, Alhamzawi, R
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Quantile regression, asset pricing and investment decision
IIMB Management Review, 2021 The present study compares the Fama-French three factor coefficient estimates obtained from both ordinary least squares (OLS) and quantile regression for 25 size-value sorted portfolios of BSE 500.
Moinak Maiti
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Factors Affecting Productivity of Upland and Lowland Rice Farms in Matalom, Leyte: A Quantile Regression Approach [PDF]
Review of Socio-Economic Research and Development Studies, 2017 This study investigates the determinants of productivity in selected upland and lowland rice farms in Matalom, Leyte using quantile regression approach. Data on rice production are obtained from 40 upland and 40 lowland rice farming households which are ...
Brenda M. Ramoneda, Junnel K. Pene
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Local quantile regression [PDF]
Journal of Statistical Planning and Inference, 2013 Quantile regression is a technique to estimate conditional quantile curves. It provides a comprehensive picture of a response contingent on explanatory variables. In a flexible modeling framework, a specific form of the conditional quantile curve is not a priori fixed.
Wolfgang Karl Härdle +2 more
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Smoothing Quantile Regressions [PDF]
Journal of Business & Economic Statistics, 2019 We propose to smooth the entire objective function, rather than only the check function, in a linear quantile regression context. Not only does the resulting smoothed quantile regression estimator yield a lower mean squared error and a more accurate Bahadur-Kiefer representation than the standard estimator, but it is also asymptotically differentiable.
Marcelo Fernandes +2 more
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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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CoRR, 2019 Quantile regression is a tool for learning conditional distributions. In this paper we study quantile regression in the setting where a protected attribute is unavailable when fitting the model. This can lead to "unfair'' quantile estimators for which the effective quantiles are very different for the subpopulations defined by the protected attribute ...
Dana Yang, John Lafferty, David Pollard
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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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Quantile cointegrating regression [PDF]
Journal of Econometrics, 2009 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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