Group Identification and Variable Selection in Quantile Regression by Ali Alkenani, Basim Shlaibah Msallam

“…Using the Pairwise Absolute Clustering and Sparsity (PACS) penalty, we proposed the regularized quantile regression QR method (QR-PACS). The PACS penalty achieves the elimination of insignificant predictors and the combination of predictors with indistinguishable coefficients (IC), which are the two issues raised in the searching for the true model. …”
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Power Prior Elicitation in Bayesian Quantile Regression by Rahim Alhamzawi, Keming Yu

“…We address a quantile dependent prior for Bayesian quantile regression. We extend the idea of the power prior distribution in Bayesian quantile regression by employing the likelihood function that is based on a location-scale mixture representation of the asymmetric Laplace distribution. …”
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Quantile Regression and Beyond in Statistical Analysis of Data by Rahim Alhamzawi, Keming Yu, Himel Mallick

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The Determinants of Fish Catch: A Quantile Regression Approach by Mary Pleños

Subjects: “…quantile regression…”
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Communication-Efficient Modeling with Penalized Quantile Regression for Distributed Data by Aijun Hu, Chujin Li, Jing Wu

“…In order to deal with high-dimensional distributed data, this article develops a novel and communication-efficient approach for sparse and high-dimensional data with the penalized quantile regression. In each round, the proposed method only requires the master machine to deal with a sparse penalized quantile regression which could be realized fastly by proximal alternating direction method of multipliers (ADMM) algorithm and the other worker machines to compute the subgradient on local data. …”
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Unit-Chen distribution and its quantile regression model with applications by Ammar M. Sarhan

“…The statistical properties of the proposed distribution are discussed, along with a quantile regression model based on the unit-Chen distribution. …”
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INCOME AND CRIME RELATIONSHIP IN THE RECESSION PERIOD: A QUANTILE REGRESSION APPROACH by Filiz Guneysu Atasoy

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The relationship between fiscal federalism and fiscal discipline: panel quantile regression approach by Kayode Olaide, Josine Uwilingiye, Beatrice D. Simo-Kengne

“…But, despite the lack of unanimity in the theoretical discourse on the fiscal discipline impact of fiscal federalism, empirical studies on the relationship are still scarce and mixed in conclusions. Using a panel quantile regression estimation approach for a sample of twenty countries over the period 1996–2018, this study establishes that the impact of fiscal federalism on fiscal discipline may not be constant on the conditional mean of the fiscal discipline but varies along its conditional distribution. …”
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Quantile Regression for Probabilistic Electricity Price Forecasting in the U.K. Electricity Market by Yuki Osone, Daisuke Kodaira

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A novel quantile regression for fractiles based on unit logistic exponential distribution by Hanan Haj Ahmad, Kariema A. Elnagar

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Analysis of the Influence of Quantile Regression Model on Mainland Tourists’ Service Satisfaction Performance by Wen-Cheng Wang, Wen-Chien Cho, Yin-Jen Chen

“…The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models.…”
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Explaining Obesity- and Smoking-related Healthcare Costs through Unconditional Quantile Regression by Bijan Borah, James Naessens, Kerry Olsen, Nilay Shah

“…**Objectives:** Unlike previous studies, this study evaluates the distributional effects of obesity and smoking on healthcare cost distribution using a recently developed econometric framework: the unconditional quantile regression (UQR). **Methods:** Results were compared with the traditional conditional quantile regression (CQR), and the generalized linear modeling (GLM) framework that is commonly used for modeling healthcare cost. …”
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Personality Meets Technology: Quantile Regression Analysis of Openness to Experience in Digital Wallet Usage by Fildzah Rudyah Putri, Mahdiyah Mahdiyah, Faisal Madani, Mutiara Dahlia

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Empirical Mode Decomposition Combined with Local Linear Quantile Regression for Automatic Boundary Correction by Abobaker M. Jaber, Mohd Tahir Ismail, Alssaidi M. Altaher

“…At the first stage, local polynomial quantile regression (LLQ) is applied to provide an efficient description of the corrupted and noisy data. …”
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Unified Quantile Regression Deep Neural Network with Time-Cognition for Probabilistic Residential Load Forecasting by Zhuofu Deng, Binbin Wang, Heng Guo, Chengwei Chai, Yanze Wang, Zhiliang Zhu

“…In this paper, we propose a unified quantile regression deep neural network with time-cognition for tackling this challenging issue. …”
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Lithium Battery Life Prediction for Electric Vehicles Using Enhanced TCN and SVN Quantile Regression by Xinyue Li, Jiangwei Chu

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Relativistic Electron Flux Prediction at Geosynchronous Orbit Based on the Neural Network and the Quantile Regression Method by Hui Zhang, Suiyan Fu, Lun Xie, Duo Zhao, Chao Yue, Zuyin Pu, Ying Xiong, Tong Wu, Shaojie Zhao, Yixin Sun, Bo Cui, Zhekai Luo

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New Link Functions for Distribution–Specific Quantile Regression Based on Vector Generalized Linear and Additive Models by V. F. Miranda-Soberanis, T. W. Yee

“…In the usual quantile regression setting, the distribution of the response given the explanatory variables is unspecified. …”
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A Comparison of Mean-Based and Quantile Regression Methods for Analyzing Self-Report Dietary Intake Data by Michelle L. Vidoni, Belinda M. Reininger, MinJae Lee

“…at Home Intervention, we aimed to compare traditional mean-based regression to quantile regression for describing the impact of a health behavior intervention on healthy and unhealthy eating indices. …”
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Investigation into Interactions between Accident Consequences and Traffic Signs: A Bayesian Bivariate Tobit Quantile Regression Approach by Xuecai Xu, Željko Šarić

“…To address the correlation and heterogeneity, Bayesian bivariate Tobit quantile regression models were proposed, in which the bivariate framework addressed the correlation of residuals with Bayesian approach, while the Tobit quantile regression model accommodated the heterogeneity due to unobserved factors. …”
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