Multiple-output quantile regression neural network
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Publication:6547755
DOI10.1007/S11222-024-10408-6zbMATH Open1539.6202MaRDI QIDQ6547755
Publication date: 31 May 2024
Published in: Statistics and Computing (Search for Journal in Brave)
quantile regressionoptimal transport mapmultivariate responsesconditional quantile contours and regionsinput convex neural network
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Cites Work
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- Single-index quantile regression
- On elliptical quantiles in the quantile regression setup
- Computing multiple-output regression quantile regions
- Semiparametric Gaussian copula models: geometry and efficient rank-based estimation
- Monge-Kantorovich depth, quantiles, ranks and signs
- Bayesian quantile regression for single-index models
- Elliptical multiple-output quantile regression and convex optimization
- General notions of statistical depth function.
- Existence and uniqueness of monotone measure-preserving maps
- Tests for high-dimensional data based on means, spatial signs and spatial ranks
- Distribution and quantile functions, ranks and signs in dimension \(d\): a measure transportation approach
- Noncrossing structured additive multiple-output Bayesian quantile regression models
- Robust clustering tools based on optimal transportation
- On generalized elliptical quantiles in the nonlinear quantile regression setup
- On the \(u\)\,th geometric conditional quantile
- Multivariate quantiles and multiple-output regression quantiles: from \(L_{1}\) optimization to halfspace depth
- The spatial distribution in infinite dimensional spaces and related quantiles and depths
- Local bilinear multiple-output quantile/depth regression
- On a Geometric Notion of Quantiles for Multivariate Data
- Local Linear Quantile Regression
- Polar factorization and monotone rearrangement of vector‐valued functions
- Regression Quantiles
- Sign Tests in Multidimension: Inference Based on the Geometry of the Data Cloud
- Bayesian quantile regression for partially linear additive models
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