A convergent algorithm for quantile regression with smoothing splines
Computational Statistics & Data Analysis
The nature of statistical learning theory
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Semiparametric support vector and linear programming machines
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Localized Rademacher Complexities
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Sparse bayesian learning and the relevance vector machine
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Rademacher and gaussian complexities: risk bounds and structural results
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Bi-level path following for cross validated solution of kernel quantile regression
Proceedings of the 25th international conference on Machine learning
A New Probabilistic Approach in Rank Regression with Optimal Bayesian Partitioning
The Journal of Machine Learning Research
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Support vector censored quantile regression under random censoring
Computational Statistics & Data Analysis
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IEEE Transactions on Information Theory
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Bi-Level Path Following for Cross Validated Solution of Kernel Quantile Regression
The Journal of Machine Learning Research
Bundle Methods for Regularized Risk Minimization
The Journal of Machine Learning Research
The entire quantile path of a risk-agnostic SVM classifier
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Computational Statistics & Data Analysis
Bayesian inference for additive mixed quantile regression models
Computational Statistics & Data Analysis
On Convergence of Kernel Learning Estimators
SIAM Journal on Optimization
QBoost: Predicting quantiles with boosting for regression and binary classification
Expert Systems with Applications: An International Journal
The structured elastic net for quantile regression and support vector classification
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Transductive gaussian process regression with automatic model selection
ECML'06 Proceedings of the 17th European conference on Machine Learning
Multivariate convex support vector regression with semidefinite programming
Knowledge-Based Systems
The Journal of Machine Learning Research
Bound the learning rates with generalized gradients
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Nonparametric bivariate copula estimation based on shape-restricted support vector regression
Knowledge-Based Systems
Fast learning rates for sparse quantile regression problem
Neurocomputing
Learning uncertainty models from weather forecast performance databases using quantile regression
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Smooth Nonparametric Copula Estimation with Least Squares Support Vector Regression
Neural Processing Letters
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Neural Networks
Modeling financial dependence with support vector regression
Intelligent Data Analysis
Information Sciences: an International Journal
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In regression, the desired estimate of y|x 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 y|x, will be below the estimate. For τ = 0.5 this is an estimate of the median. What 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 estimator. Experimental results show the feasibility of the approach and competitiveness of our method with existing ones. We discuss several types of extensions including an approach to solve the quantile crossing problems, as well as a method to incorporate prior qualitative knowledge such as monotonicity constraints.