Choosing summary statistics by least angle regression for approximate Bayesian computation

Muhammad Faisal, Andreas Futschik, Ijaz Hussain and Mitwali Abd-el.Moemen

Journal of Applied Statistics, 2016, vol. 43, issue 12, 2191-2202

Abstract: Bayesian statistical inference relies on the posterior distribution. Depending on the model, the posterior can be more or less difficult to derive. In recent years, there has been a lot of interest in complex settings where the likelihood is analytically intractable. In such situations, approximate Bayesian computation (ABC) provides an attractive way of carrying out Bayesian inference. For obtaining reliable posterior estimates however, it is important to keep the approximation errors small in ABC. The choice of an appropriate set of summary statistics plays a crucial role in this effort. Here, we report the development of a new algorithm that is based on least angle regression for choosing summary statistics. In two population genetic examples, the performance of the new algorithm is better than a previously proposed approach that uses partial least squares.

Date: 2016
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DOI: 10.1080/02664763.2015.1134447

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