Synthetic likelihood in misspecified models-Reference-Cited by-同舟云学术

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Synthetic likelihood in misspecified models

Published:2024-06-24 Issue: Volume: Page:1-23
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Frazier David T.¹,Nott David J.²³,Drovandi Christopher⁴

Affiliation:

1. Department of Econometrics and Business Statistics, Monash University, Clayton VIC 3800, Australia

2. Department of Statistics and Applied Probability, National University of Singapore, Singapore 117546

3. Operations Research and Analytics Cluster, National University of Singapore, Singapore 119077

4. School of Mathematical Sciences, Queensland University of Technology, Brisbane 4000 Australia

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2024.2370594

Reference25 articles.

1. An Z. D. J. Nott and C. Drovandi (2020). Robust Bayesian synthetic likelihood via a semi-parametric approach. Statistics and Computing 30 (3) 543–557.

2. An Z. L. F. South and C. Drovandi (2022). BSL: An R package for efficient parameter estimation for simulation-based models via Bayesian synthetic likelihood. Journal of Statistical Software 101 1–33.

3. Andrieu C. and G. O. Roberts (2009). The pseudo-marginal approach for efficient monte carlo computations. The Annals of Statistics 37 (2) 697–725.

4. Bhattacharya A. D. Pati Y. Yang et al. (2019). Bayesian fractional posteriors. The Annals of Statistics 47 (1) 39–66.

5. Bissiri P. G. C. C. Holmes and S. G. Walker (2016). A general framework for updating belief distributions. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 78 (5) 1103.

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