arXiv 25 Jun 2020 · Statistics — Methodology · 8 citations (OpenAlex)
arXiv:2006.14126 · PDF · DOI · OpenAlex · Extracted main text
In many instances, the application of approximate Bayesian methods is hampered by two practical features: 1) the requirement to project the data down to low-dimensional summary, including the choice of this projection, which ultimately yields inefficient inference; 2) a possible lack of robustness to deviations from the underlying model structure. Motivated by these efficiency and robustness concerns, we construct a new Bayesian method that can deliver efficient estimators when the underlying model is well-specified, and which is simultaneously robust to certain forms of model misspecification. This new approach bypasses the calculation of summaries by considering a norm between empirical and simulated probability measures. For specific choices of the norm, we demonstrate that this approach can deliver point estimators that are as efficient as those obtained using exact Bayesian inference, while also simultaneously displaying robustness to deviations from the underlying model assumptions.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Rieder, H (2012) Robust asymptotic statistics, volume 1 | 1.000 | 9 | 3 | 100% |
| 2 | Frazier, D. T., Robert, C. P., and Rousseau, J (2020) Model misspecification in approximate bayesian computation: consequences and diagnostics self | 1.000 | 8 | 3 | 100% |
| 3 | Bernton, E., Jacob, P. E., Gerber, M., and Robert, C. P (2019) Approximate Bayesian computation with the Wasserstein distance | 1.000 | 6 | 4 | 100% |
| 4 | Frazier, D. T., Martin, G. M., Robert, C. P., and Rousseau, J (2018) Asymptotic properties of approximate Bayesian computation self | 1.000 | 6 | 3 | 100% |
| 5 | Donoho, D. L. and Liu, R. C (1988) The" automatic" robustness of minimum distance functionals | 0.928 | 4 | 3 | 100% |
| 6 | Fearnhead, P. and Prangle, D (2012) Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation | 0.737 | 3 | 2 | 100% |
| 7 | Beran, R. et al (1977) Minimum hellinger distance estimates for parametric models | 0.737 | 3 | 2 | 100% |
| 8 | Del Moral, P., Doucet, A., and Jasra, A (2012) An adaptive sequential monte carlo method for approximate bayesian computation | 0.737 | 3 | 2 | 100% |
| 9 | Li, W. and Fearnhead, P (2018) On the asymptotic efficiency of approximate Bayesian computation estimators | 0.644 | 2 | 2 | 100% |
| 10 | Basu, A. and Lindsay, B. G (1994) Minimum disparity estimation for continuous models: efficiency, distributions and robustness | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 20 scored citations.