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Can Food Inflation Be Stabilized By Monetary Policy? A Quantile Regression Approach
Journal of economic impact, 2022 Theoretically, a consistent and well-defined monetary policy can stabilize food inflation. However, empirical findings have reported both positive and negative effects of monetary policy measures on food inflation.
Choudary Ihtasham Ali +5 more
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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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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 conditions of the form Eg(y, θ) = 0 where θ is a k dimensional parameter of interest and k may be smaller ...
Tony Lancaster, Sung Jae Jun
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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.
Eustasio del Barrio +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 multivariate responses with covariates. The factor structure is allowed to vary with the quantile levels, which is more flexible than the classical factor models.
Chao, Shih-Kang +2 more
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Distributional Reinforcement Learning with Quantile Regression [PDF]
AAAI Conference on Artificial Intelligence, 2017 In reinforcement learning (RL), an agent interacts with the environment by taking actions and observing the next state and reward. When sampled probabilistically, these state transitions, rewards, and actions can all induce randomness in the observed
Will Dabney +3 more
semanticscholar +3 more sources
Scalable Membership Inference Attacks via Quantile Regression [PDF]
Neural Information Processing Systems, 2023 Membership inference attacks are designed to determine, using black box access to trained models, whether a particular example was used in training or not. Membership inference can be formalized as a hypothesis testing problem.
Martín Bertrán +5 more
semanticscholar +1 more source
Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure [PDF]
Journal of business & economic statistics, 2023 This article considers estimating functional-coefficient models in panel quantile regression with individual effects, allowing the cross-sectional and temporal dependence for large panel observations.
Xiao-rong Yang +3 more
semanticscholar +1 more source
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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