The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
A Comparative Review of Dimension Reduction Methods in Approximate Bayesian Computation
Open accessAbstract
Approximate Bayesian computation (ABC) methods make use of comparisons between simulated and observed summary statistics to overcome the problem of computationally intractable likelihood functions. As the practical implementation of ABC requires computations based on vectors of summary statistics, rather than full data sets, a central question is how to derive low-dimensional summary statistics from the observed data with minimal loss of information. In this article we provide a comprehensive review and comparison of the performance of the principal methods of dimension reduction proposed in the ABC literature. The methods are split into three nonmutually exclusive classes consisting of best subset selection methods, projection techniques and regularization. In addition, we introduce two new methods of dimension reduction. The first is a best subset selection method based on Akaike and Bayesian information criteria, and the second uses ridge regression as a regularization procedure. We illustrate the performance of these dimension reduction techniques through the analysis of three challenging models and data sets.
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Cites
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BackgroundMethodApproximate Bayesian computation in population genetics
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Cited by
Approximate Bayesian Computation
Sunnåker, M., Busetto, A. G., Numminen, E., Corander, J., Foll, M., & Dessimoz, C. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002803 · Cited by 623
Supervised Machine Learning for Population Genetics: A New Paradigm
Schrider, D. R., & Kern, A. D. (2018) · Trends in Genetics · doi:10.1016/j.tig.2017.12.005 · Cited by 453
Smolyanskaya, O. A., Chernomyrdin, N. V., Konovko, A. A., Zaytsev, K. I., Ozheredov, I. A., Cherkasova, O. P., Nazarov, M. M., Guillet, J.-P., Kozlov, S. A., Kistenev, Y. V., et al. (2018) · Progress in Quantum Electronics · doi:10.1016/j.pquantelec.2018.10.001 · Cited by 292
Boitard, S., Rodríguez, W., Jay, F., Mona, S., & Austerlitz, F. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1005877 · Cited by 190
Metadata
- Authors
- Blum, M. G. B., Nunes, M. A., Prangle, D., & Sisson, S. A.
- Venue
- Statistical Science
- Publisher
- Institute of Mathematical Statistics
- Published
- 2013-05-01
- Volume
- 28
- Issue
- 2
- Pages
- 189-208
- ISSN
- 0883-4237 (ISSN-L)
- OpenAlex
- W2123368367
- Cited by
- 353
- References
- 66
- FWCI
- 26.38
- Type
- journal article
- Language
- en
- Open access
- bronze
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