Cover Image

A Bayesian shrinkage estimator for transfer learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Abba, Mohamed A., Williams, Jonathan P., Reich, Brian J.
Format:	Preprint
Published:	2024
Subjects:	Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Amortized Bayesian Local Interpolation NetworK: Fast covariance parameter estimation for Gaussian Processes
by: Feng, Brandon R., et al.
Published: (2024)

Stochastic Gradient MCMC for Massive Geostatistical Data
by: Abba, Mohamed A., et al.
Published: (2024)

On Bayesian wavelet shrinkage estimation of nonparametric regression models with stationary errors
by: Sousa, Alex Rodrigo dos S., et al.
Published: (2024)

Bayesian shrinkage priors for penalized synthetic control estimators in the presence of spillovers
by: Fernández-Morales, Esteban, et al.
Published: (2025)

Unified implementation and comparison of Bayesian shrinkage methods for treatment effect estimation in subgroups
by: Wolbers, Marcel, et al.
Published: (2026)

A nonstationary spatial model of PM2.5 with localized transfer learning from numerical model output
by: Gong, Wenlong, et al.
Published: (2025)

A class of priors to perform asymmetric Bayesian wavelet shrinkage
by: Sousa, Alex Rodrigo dos Santos
Published: (2024)

Bayesian factorization via $L_{1/2}$ shrinkage
by: Liu, Shicheng, et al.
Published: (2026)

Bayesian non-linear subspace shrinkage using horseshoe priors
by: Duda, Julia Christin, et al.
Published: (2024)

Bayesian shrinkage priors subject to linear constraints
by: Ling, Zhi, et al.
Published: (2025)

Fitted value shrinkage
by: Ham, Daeyoung, et al.
Published: (2023)

Multivariate and Online Transfer Learning with Uncertainty Quantification
by: Hickey, Jimmy, et al.
Published: (2024)

Tail-adaptive Bayesian shrinkage
by: Lee, Se Yoon, et al.
Published: (2020)

Bayesian wavelet shrinkage for low SNR data based on the Epanechnikov kernel
by: Barrios, Fidel Aniano Causil, et al.
Published: (2025)

Learning discrete Bayesian networks with hierarchical Dirichlet shrinkage
by: Dombowsky, Alexander, et al.
Published: (2025)

Spatiotemporal double machine learning to estimate the impact of Cambodian land concessions on deforestation
by: Arifin, Anika, et al.
Published: (2026)

A Beta Cauchy-Cauchy (BECCA) shrinkage prior for Bayesian variable selection
by: Rodrigo, Linduni M., et al.
Published: (2025)

Calibrating Bayesian Inference
by: Liu, Yang, et al.
Published: (2025)

A Bi-failure Mode Model for Competing Risk Modeling with HMC-Driven Bayesian Framework
by: Abba, Badamasi, et al.
Published: (2025)

Spike-and-slab shrinkage priors for structurally sparse Bayesian neural networks
by: Jantre, Sanket, et al.
Published: (2023)

Global-local shrinkage priors for modeling random effects in multivariate spatial small area estimation
by: Nishina, Shushi, et al.
Published: (2026)

Improving time series estimation and prediction via transfer learning
by: Lin, Yuchang, et al.
Published: (2025)

Posterior shrinkage towards linear subspaces
by: Sewell, Daniel K.
Published: (2024)

Optimal shrinkage estimation in heteroscedastic hierarchical linear models
by: Kou, Samuel, et al.
Published: (2015)

A Deep Learning Synthetic Likelihood Approximation of a Non-stationary Spatial Model for Extreme Streamflow Forecasting
by: Majumder, Reetam, et al.
Published: (2022)

Wavelet shrinkage based on the raised cosine prior
by: Reina, Juliana Marchesi, et al.
Published: (2025)

Two-stage Estimators for Spatial Confounding with Point-Referenced Data
by: Wiecha, Nate, et al.
Published: (2024)

Spatial Causal Tensor Completion for Multiple Exposures and Outcomes: An Application to the Health Effects of PFAS Pollution
by: Zhou, Xiaodan, et al.
Published: (2026)

A note on wavelet shrinkage in nonparametric regression models with ARFIMA errors
by: Sousa, Alex Rodrigo dos S., et al.
Published: (2025)

Modeling Extremal Streamflow using Deep Learning Approximations and a Flexible Spatial Process
by: Majumder, Reetam, et al.
Published: (2022)

The R2D2 Prior for Generalized Linear Mixed Models
by: Yanchenko, Eric, et al.
Published: (2021)

Semiparametric Estimation of the Shape of the Limiting Bivariate Point Cloud
by: Majumder, Reetam, et al.
Published: (2023)

When are novel methods for analyzing complex chemical mixtures in epidemiology beneficial?
by: Wiecha, Nate, et al.
Published: (2025)

Combined shrinkage of fixed and random effects in linear mixed models using empirical Bayes
by: Amestoy, Matteo, et al.
Published: (2026)

The Pairwise Matching Design is Optimal under Extreme Noise and Assignments
by: Azriel, David, et al.
Published: (2024)

Block Designs that Provide Optimal Power in the Cochran-Mantel-Haenszel Test
by: Azriel, David, et al.
Published: (2025)

A new mixture model for spatiotemporal exceedances with flexible tail dependence
by: Li, Ryan, et al.
Published: (2026)

Causal Spatial Quantile Regression
by: Gong, Yan, et al.
Published: (2025)

Distributed model building and recursive integration for big spatial data modeling
by: Hector, Emily C., et al.
Published: (2023)

Estimating Covariate Effects on Functional Connectivity using Voxel-Level fMRI Data
by: Zhao, Wei, et al.
Published: (2025)