Approximate Bayesian computation has become an essential tool for the analysis of complex stochastic models when the likelihood function is numerically unavailable. However, the well-established statistical method of empirical likelihood provides another route to such settings that bypasses simulations from the model and the choices of the approximate Bayesian computation parameters (summary statistics, distance, tolerance), while being convergent in the number of observations. Furthermore, bypassing model simulations may lead to significant time savings in complex models, for instance those found in population genetics. The Bayesian computation with empirical likelihood algorithm we develop in this paper also provides an evaluation of its own performance through an associated effective sample size. The method is illustrated using several examples, including estimation of standard distributions, time series, and population genetics models.
This research output has an Altmetric Attention Score of 25. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 October 2018.
All research outputs
#1,990,464
of 34,763,505 outputs
Outputs from Proceedings of the National Academy of Sciences of the United States of America
#24,746
of 119,650 outputs
Outputs of similar age
#15,460
of 351,685 outputs
Outputs of similar age from Proceedings of the National Academy of Sciences of the United States of America
#225
of 1,029 outputs
Altmetric has tracked 34,763,505 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 119,650 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 40.2. This one has done well, scoring higher than 79% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 351,685 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 1,029 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.
