A novel approach for choosing summary statistics in approximate Bayesian computation.

ABSTRACT: The choice of summary statistics is a crucial step in approximate Bayesian computation (ABC). Since statistics are often not sufficient, this choice involves a trade-off between loss of information and reduction of dimensionality. The latter may increase the efficiency of ABC. Here, we propose an approach for choosing summary statistics based on boosting, a technique from the machine-learning literature. We consider different types of boosting and compare them to partial least-squares regression as an alternative. To mitigate the lack of sufficiency, we also propose an approach for choosing summary statistics locally, in the putative neighborhood of the true parameter value. We study a demographic model motivated by the reintroduction of Alpine ibex (Capra ibex) into the Swiss Alps. The pa

SUBMITTER: Aeschbacher S 

PROVIDER: S-EPMC3522150 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature