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Spatial Quantile Regression [PDF]
Comparative Economic Research, 2012 In a number of applications, a crucial problem consists in describing and analyzing the influence of a vector Xi of covariates on some real-valued response variable Yi.
Grażyna Trzpiot
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Local Quantile Regression [PDF]
Journal of Statistical Planning and Inference, 2010 Quantile regression is a technique to estimate conditional quantile curves. It provides a comprehensive picture of a response contingent on explanatory variables.
Härdle, Wolfgang Karl +2 more
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Factorisable Multitask Quantile Regression [PDF]
SSRN Electronic Journal, 2020 A multivariate quantile regression model with a factor structure is proposed to study data with many responses of interest. The factor structure is allowed to vary with the quantile levels, which makes our framework more flexible than the classical ...
Chao, Shih-Kang +2 more
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Bayesian quantile regression [PDF]
Working Paper Series, 2005 Recent work by Schennach (2005) has opened the way to a Bayesian treatment of quantile regression. Her method, called Bayesian exponentially tilted empirical likelihood (BETEL), provides a likelihood for data y subject only to a set of m moment ...
Sung Jae Jun, Tony Lancaster
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Censored Quantile Regression Redux [PDF]
Journal of Statistical Software, 2008 Quantile regression for censored survival (duration) data offers a more flexible alternative to the Cox proportional hazard model for some applications. We describe three estimation methods for such applications that have been recently incorporated into ...
Roger Koenker
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Stress Testing German Industry Sectors: Results from a Vine Copula Based Quantile Regression
Risks, 2017 Measuring interdependence between probabilities of default (PDs) in different industry sectors of an economy plays a crucial role in financial stress testing. Thereby, regression approaches may be employed to model the impact of stressed industry sectors
Matthias Fischer +3 more
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Sparse Quantile Regression [PDF]
Journal of Econometrics, 2020 We consider both $\ell _{0}$-penalized and $\ell _{0}$-constrained quantile regression estimators. For the $\ell _{0}$-penalized estimator, we derive an exponential inequality on the tail probability of excess quantile prediction risk and apply it to obtain non-asymptotic upper bounds on the mean-square parameter and regression function estimation ...
Lee, Sokbae (Simon), Chen, Le-Yu
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Function-on-function linear quantile regression
Mathematical Modelling and Analysis, 2022 In this study, we propose a function-on-function linear quantile regression model that allows for more than one functional predictor to establish a more flexible and robust approach. The proposed model is first transformed into a finitedimensional space
Ufuk Beyaztas, Han Lin Shang
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Quantile Regression with Generated Regressors
Econometrics, 2021 This paper studies estimation and inference for linear quantile regression models with generated regressors. We suggest a practical two-step estimation procedure, where the generated regressors are computed in the first step. The asymptotic properties of
Liqiong Chen +2 more
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Desimal, 2023 The censored quantile regression method is a parameter estimation method that can be used to overcome censored data and BLUE (Best Linear Unbiased Estimator) assumptions that are not met.
Sarmada Sarmada +2 more
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