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Modelbased approaches to nonparametric Bayesian quantile regression
"... In several regression applications, a different structural relationship might be anticipated for the higher or lower responses than the average responses. In such cases, quantile regression analysis can uncover important features that would likely be overlooked by mean regression. We develop two dis ..."
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Cited by 1 (1 self)
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distinct Bayesian approaches to fully nonparametric modelbased quantile regression. The first approach utilizes an additive regression framework with Gaussian process priors for the quantile regression functions and a scale uniform Dirichlet process mixture prior for the error distribution, which yields
A Bayesian nonparametric approach to inference for quantile regression
 Journal of Business and Economic Statistics
, 2009
"... In several regression applications, a dierent structural relationship might be anticipated for the higher or lower responses than the average responses. In such cases, quantile regression analysis can uncover important features that would likely be overlooked by traditional mean regression. We devel ..."
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Cited by 14 (8 self)
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develop a Bayesian method for fully nonparametric modelbased quantile regression. The approach involves exible Dirichlet process mixture models for the joint distribution of the response and the covariates, with posterior inference for dierent quantile curves emerging from the conditional distribution
Nonparametric Mixture and Quantile Regression with Applications
"... Conditional density estimation and quantile regression are techniques that allow for a better understanding of the relationship between a response variable and a set of covariates in comparison with usual regression methods. Therefore, these techniques are of great importance in many scientific fiel ..."
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double kernel approach is presented as well. Second, I develop nonparametric errorinvariables quantile regression based on existing quantile regression methods available for the errorfree case. Scientific Motivation Through the years, astronomers have had varying ideas about the evolution
Nonparametric Mquantile Regression via Penalized Splines
"... Quantile regression investigates the conditional quantile functions of a response variables in terms of a set of covariates. Mquantile regression extends this idea by a “quantilelike ” generalization of regression based on influence functions. In this work we extend it to nonparametric regressio ..."
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Quantile regression investigates the conditional quantile functions of a response variables in terms of a set of covariates. Mquantile regression extends this idea by a “quantilelike ” generalization of regression based on influence functions. In this work we extend it to nonparametric
Title Quantile Regression
, 2015
"... Description Estimation and inference methods for models of conditional quantiles: Linear and nonlinear parametric and nonparametric (total variation penalized) models for conditional quantiles of a univariate response and several methods for handling censored survival data. Portfolio selection meth ..."
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Description Estimation and inference methods for models of conditional quantiles: Linear and nonlinear parametric and nonparametric (total variation penalized) models for conditional quantiles of a univariate response and several methods for handling censored survival data. Portfolio selection
Nonparametric quantile estimation
, 2006
"... In regression, the desired estimate of yx is not always given by a conditional mean, although this is most common. Sometimes one wants to obtain a good estimate that satisfies the property that a proportion, τ, of yx, will be below the estimate. For τ = 0.5 this is an estimate of the median. What ..."
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Cited by 55 (9 self)
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might be called median regression, is subsumed under the term quantile regression. We present a nonparametric version of a quantile estimator, which can be obtained by solving a simple quadratic programming problem and provide uniform convergence statements and bounds on the quantile property of our
A.J.: Nonparametric quantile regression
 Journal of Machine Learning Research
"... In regression, the desired estimate of yx is not always given by a conditional mean, although this is most common. Sometimes one wants to obtain a good estimate that satisfies the property that a proportion, τ, of yx, will be below the estimate. For τ = 0.5 this is an estimate of the median. What ..."
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Cited by 7 (1 self)
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might be called median regression, is subsumed under the term quantile regression. We present a nonparametric version of a quantile estimator, which can be obtained by solving a simple quadratic programming problem and provide uniform convergence statements and bounds on the quantile property of our
Powerful nonparametric checks for quantile regression
, 2014
"... We address the issue of lackoffit testing for a parametric quantile regression. We propose a simple test that involves onedimensional kernel smoothing, so that the rate at which it detects local alternatives is independent of the number of covariates. The test has asymptotically gaussian critical ..."
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We address the issue of lackoffit testing for a parametric quantile regression. We propose a simple test that involves onedimensional kernel smoothing, so that the rate at which it detects local alternatives is independent of the number of covariates. The test has asymptotically gaussian
Testing for covariate balance using nonparametric quantile regression and resampling methods
"... Abstract: Consistency of propensity score matching estimators hinges on the propensity score’s ability to balance the covariates among treated and nontreated units. Conventional balance tests merely check for differences in covariates ’ means, but cannot account for differences in higher moments. S ..."
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Cited by 2 (0 self)
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. Specification tests constitute an alternative, but might reject misspecified, but yet balancing propensity score models. This paper proposes balance tests based on (i) nonparametric quantile regression to check for differences in the entire distributions of covariates and (ii) resampling methods
Results 1  10
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