Fast maximum likelihood estimation for general hierarchical models-Reference-Cited by-同舟云学术

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Fast maximum likelihood estimation for general hierarchical models

Published:2024-07-24 Issue: Volume: Page:1-29
ISSN:0266-4763
Container-title:Journal of Applied Statistics
language:en
Short-container-title:Journal of Applied Statistics

Author:

Hong Johnny¹,Stoudt Sara²^ORCID,de Valpine Perry³

Affiliation:

1. Department of Statistics, University of California Berkeley, Berkeley, CA, USA

2. Department of Mathematics, Bucknell University, Lewisburg, PA, USA

3. Department of Environmental Science, Policy and Management, University of California, Berkeley, California, USA

Funder

National Science Foundation through Research Training Groups

Gordon and Betty Moore Foundation

Alfred P. Sloan Foundation

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/02664763.2024.2383284

Reference76 articles.

1. M.A. Abba B.J. Reich R. Majumder and B. Feng Stochastic gradient mcmc for massive geostatistical data preprint (2024) arXiv:2405.04531.

2. Stochastic Modelling and Applied Probability;Asmussen S.,2007

3. C. Baey M. Delattre E. Kuhn J.B. Leger and S. Lemler Efficient preconditioned stochastic gradient descent for estimation in latent variable models in International Conference on Machine Learning PMLR 2023 pp. 1430–1453.

4. Fitting linear mixed-effects models using lme4;Bates D.;J. Stat. Softw.,2015

5. The variational Bayesian EM algorithm for incomplete data: With application to scoring graphiclal model structures;Beal M.J.;Bayesian Anal.,2003

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