Likelihood tempering in dynamic model averaging

0477043 - ÚTIA 2018 RIV CH eng C - Conference Paper (international conference)
Reichl, Jan - Dedecius, Kamil
Likelihood tempering in dynamic model averaging.
Bayesian Statistics in Action. Cham: Springer International Publishing, 2017 - (Argiento, R.; Lanzarone, E.; Villalobos, I.; Mattei, A.), s. 67-77. ISBN 978-3-319-54083-2. ISSN 2194-1009.
[Bayesian Young Statisticians Meeting, BAYSM 2016. Florence (IT), 19.06.2016-21.06.2016]
R&D Projects: GA ČR(CZ) GP14-06678P
Institutional support: RVO:67985556
Keywords : Model averaging * Model uncertainty * Prediction * Tempered likelihood
OECD category: Statistics and probability
http://library.utia.cas.cz/separaty/2017/AS/dedecius-0477043.pdf

We study the problem of online prediction with a set of candidate models using dynamic model averaging procedures. The standard assumptions of model averaging state that the set of admissible models contains the true one(s), and that these models are continuously updated by valid data. However, both these assumptions are often violated in practice. The models used for online tasks are often more or less misspecified and the data corrupted (which is, mathematically, a demonstration of the same problem). Both these factors negatively influence the Bayesian inference and the resulting predictions. In this paper, we propose to suppress these issues by extending the Bayesian update by a sort of likelihood tempering, moderating the impact of observed data to inference. The method is compared to the generic dynamic model averaging and to an alternative solution via sequential quasi-Bayesian mixture modeling.
Permanent Link: http://hdl.handle.net/11104/0274025