ArticleStatistics in medicine2025
Gaussian Process Regression for Value-Censored Functional and Longitudinal Data.
Article in Statistics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Gaussian Process Regression for Value-Censored Functional and Longitudinal Data.Statistics in medicine · 2025Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gaussian process (GP) regression is widely used for flexible and non-parametric Bayesian modeling of data arising from underlying smooth functions. This paper introduces a solution to GP regression when the observations are subject to value-based censoring. We derive exact and closed-form expressions for the conditional posterior distributions of the underlying functions in both the single-curve fitting case and in the case of a hierarchical model where multiple functions are modeled simultaneously. Our method can accommodate left, right, and interval censoring, and is directly applicable as an empirical Bayes method or integrated in a Markov-Chain Monte Carlo sampler for full posterior inference. The method is validated through extensive simulations, where it substantially outperforms naive approaches that either exclude censored observations or treat them as fully observed values. We give an application to a real-world dataset of longitudinal HIV-1 RNA measurements, where the observations are subject to left censoring due to a detection limit.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.