arXiv (All)
open access
Unidentified prediction-risk rankings under spatial sampling with fixed inclusion probabilities
For Gaussian spatial prediction, we construct positive sampling mechanisms with identical observation laws and conditional site inclusion probabilities, yet opposite risk rankings for two fixed predictors. The construction selects two of four sites and allows independent Gaussian measurement error.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Spatially Adaptive Ensemble Learning with Calibrated Predictive Uncertainty#997412
- Global Sensitivity Analysis of Spatial Sets: Finite-Element-Based Estimation and the Role of Observation Windows#1000144
- Integrating local spatial outliers into context-aware spatial prediction models#398547
- How Spatial Uncertainty in Probabilistic Weather Forecasts Shapes Risk Perception#16498
- Spatial Prediction Under Uncertainty: Methodological and Computational Advances in Bayesian Maximum Entropy#373974
Record · ID 1535049
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive