arXiv (All)
open access
Fully Bayesian Spectral Clustering and Benchmarking with Uncertainty Quantification for Small Area Estimation
In this work, inspired by machine learning techniques, we propose a new Bayesian model for Small Area Estimation (SAE), the Fay-Herriot model with Spectral Clustering (FH-SC). Unlike traditional approaches, clustering in FH-SC is based on spectral clustering algorithms that utilize external covariat
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
- Latent class analysis by regularized spectral clustering#972849
- Spectral clustering in the Gaussian mixture block model#609727
- Selective Inference for Clustering Under Unknown Covariance#1342675
- Robust K-means Clustering using the Density Power Divergence Measure#799324
- Robust K-means Clustering using the Density Power Divergence Measure#797901
Record · ID 1325070
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