Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces

Ähnliche Einträge

• Conformal uncertainty quantification using kernel depth measures in separable Hilbert spaces
  von: Matabuena, Marcos, et al.
  Veröffentlicht: (2024)
• Conditional Mean and Variance Estimation via \textit{k}-NN Algorithm with Automated Variance Selection
  von: Matabuena, Marcos, et al.
  Veröffentlicht: (2024)
• Screening for Diabetes Mellitus in the U.S. Population Using Neural Network Models and Complex Survey Designs
  von: Matabuena, Marcos, et al.
  Veröffentlicht: (2024)
• Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces
  von: Lugosi, Gábor, et al.
  Veröffentlicht: (2025)
• Multivariate Scalar on Multidimensional Distribution Regression With Application to Modeling the Association Between Physical Activity and Cognitive Functions
  von: Rahul Ghosal, et al.
  Veröffentlicht: (2024)