Transfer learning from capture-recapture data improves temporal abundance and trend estimates from catch-per-unit-effort data by accounting for variable detection probabilities.
Journal of Big data , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
stat.AP 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
spBART extends BART by modeling low-dimensional covariates parametrically for interpretability and high-dimensional epigenetic predictors nonparametrically, with a CV-based variable selection procedure, achieving AUC 0.96 on multiple myeloma epigenetic data.
citing papers explorer
-
Accounting for variable detection functions in temporal abundance modeling via transfer learning
Transfer learning from capture-recapture data improves temporal abundance and trend estimates from catch-per-unit-effort data by accounting for variable detection probabilities.
-
Semi-Parametric Bayesian Additive Regression Trees for Risk Prediction with High-Dimensional Epigenetic Signatures and Low-Dimensional Covariates
spBART extends BART by modeling low-dimensional covariates parametrically for interpretability and high-dimensional epigenetic predictors nonparametrically, with a CV-based variable selection procedure, achieving AUC 0.96 on multiple myeloma epigenetic data.