arXiv (All)
open access
Linearly Constrained Generalized Linear Models with Applications in Origin-Destination Estimation
We show that data censored by linear constraints can be fit within the generalized linear model (GLM) framework, recovering the expected latent counts together with their uncertainty in a single Fisher-scoring procedure. Our motivating application is origin-destination (OD) estimation in transportat
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
- Quantifying the relationship between observed variables with censored values using Bayesian error-in-variables regression#417967
- Quantifying the relationship between observed variables with censored values using Bayesian error-in-variables regression#417953
- Shape without scale: an identifiability dichotomy for a bounded tail observed through a non-additive measurement kernel#1063595
- Doubly Robust Estimation under Covariate Dependent Censoring in Semi-Competing Risks Data#1116642
- From Good Starts to Optimal Inference: Generalized Latent Factor Models with Missingness and Implicit Regularization#999429
Record · ID 986632
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