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    Choosing summary statistics by least angle regression for approximate Bayesian computation

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    Faisal_Journal_of_Applied_Statistics.pdf (618.4Kb)
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    Publication date
    2016
    Author
    Faisal, Muhammad
    Futschik, A.
    Hussain, I.
    Abd-el.Moemen, M.
    Keyword
    Likelihood-free methods; Least angle regression; Mutation; Population genetics; Recombination
    Rights
    © 2016 Taylor & Francis. This is an Author's Original Manuscript of an article published by Taylor & Francis in the Journal of Applied Statistics on 01 Feb 2016 available online at http://www.tandfonline.com/10.1080/02664763.2015.1134447
    Peer-Reviewed
    Yes
    
    Metadata
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    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.
    URI
    http://hdl.handle.net/10454/14801
    Version
    Accepted Manuscript
    Citation
    Faisal M, Futschik A, Hussain I et al (2016) Choosing summary statistics by least angle regression for approximate Bayesian computation. Journal of Applied Statistics. 43(12): 2191-2202.
    Link to publisher’s version
    https://doi.org/10.1080/02664763.2015.1134447
    Type
    Article
    Collections
    Health Studies Publications

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