[Forum SIS] Avviso di seminario

[Brunero Liseo]

Avviso di seminario.

Il prof. Juan Miguel Marin,
Universidad Carlos III Madrid, terrà un seminario in auletta di Matematica,
primo piano facoltà di Economia alle ore 12.30 di mercoledì 18 gennaio 2017.
L'indirizzo è via del Castro Laurenziano 9, 00161 Roma.

Seguono titolo ed abstract; tutti gli interessati sono invitati a
partecipare.

A Bootstrap Likelihood approach to Bayesian Computation
Weixuan Zhu <https://arxiv.org/find/stat/1/au:+Zhu_W/0/1/0/all/0/1>, Juan
Miguel Marin <https://arxiv.org/find/stat/1/au:+Marin_J/0/1/0/all/0/1>,
Fabrizio
Leisen <https://arxiv.org/find/stat/1/au:+Leisen_F/0/1/0/all/0/1>

There is an increasing amount of literature focused on Bayesian
computational methods to address problems with intractable likelihood. One
approach is a set of algorithms known as Approximate Bayesian Computational
(ABC) methods. One of the problems of these algorithms is that the
performance depends on the tuning of some parameters, such as the summary
statistics, distance and tolerance level. To bypass this problem,
Mengersen, Pudlo and Robert (2013) introduced an alternative method based
on empirical likelihood, which can be easily implemented when a set of
constraints, related to the moments of the distribution, is known. However,
the choice of the constraints is sometimes challenging. To overcome this
problem, we propose an alternative method based on a bootstrap likelihood
approach. The method is easy to implement and in some cases it is faster
than the other approaches. The performance of the algorithm is illustrated
with examples in Population Genetics, Time Series and Stochastic
Differential Equations. Finally, we test the method on a real dataset.
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