Gaussian Process Regression for Value-Censored Functional and Longitudinal Data.

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 nai

SUBMITTER: Gorm Hoffmann A 

PROVIDER: S-EPMC12455908 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature