Journal
STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY
Volume 16, Issue 5-6, Pages 291-312Publisher
WALTER DE GRUYTER GMBH
DOI: 10.1515/sagmb-2017-0016
Keywords
approximate inference; isolation-migration model; maximum likelihood estimation; orang-utans; population genetics; stochastic approximation
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Funding
- Vienna Graduate School of Population Genetics [Austrian Science Fund (FWF)] [W1225-B20]
- Austrian Science Fund [FWF-P24551]
- Vienna Science and Technology Fund (WWTF) [MA16-061]
- National Science Foundation [NSF PHY-1125915]
- Austrian Science Fund (FWF) [P24551] Funding Source: Austrian Science Fund (FWF)
- Austrian Science Fund (FWF) [P 24551] Funding Source: researchfish
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In many population genetic problems, parameter estimation is obstructed by an intractable likelihood function. Therefore, approximate estimation methods have been developed, and with growing computational power, sampling-based methods became popular. However, these methods such as Approximate Bayesian Computation (ABC) can be inefficient in high-dimensional problems. This led to the development of more sophisticated iterative estimation methods like particle filters. Here, we propose an alternative approach that is based on stochastic approximation. By moving along a simulated gradient or ascent direction, the algorithm produces a sequence of estimates that eventually converges to the maximum likelihood estimate, given a set of observed summary statistics. This strategy does not sample much from low-likelihood regions of the parameter space, and is fast, even when many summary statistics are involved. We put considerable efforts into providing tuning guidelines that improve the robustness and lead to good performance on problems with high-dimensional summary statistics and a low signal-to-noise ratio. We then investigate the performance of our resulting approach and study its properties in simulations. Finally, we re-estimate parameters describing the demographic history of Bornean and Sumatran orang-utans.
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