| dc.creator | O’Hagan, Anthony | |
| dc.creator | Pericchi Guerra, Luis R. | |
| dc.date | 2012-11-08T22:47:45Z | |
| dc.date | 2012-11-08T22:47:45Z | |
| dc.date | 2012 | |
| dc.date.accessioned | 2017-03-17T16:53:47Z | |
| dc.date.available | 2017-03-17T16:53:47Z | |
| dc.identifier | http://hdl.handle.net/10586 /254 | |
| dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/647411 | |
| dc.description | We review a substantial literature, spanning 50 years, concerning
the resolution of conflicts using Bayesian heavy-tailed models. Conflicts arise
when different sources of information about the model parameters (e.g., prior
information, or the information in individual observations) suggest quite different
plausible regions for those parameters. Traditional Bayesian models
based on normal distributions or other conjugate structures typically resolve
conflicts by centring the posterior at some compromise position, but this is
not a realistic resolution when it means that the posterior is then in conflict
with the different information sources. Bayesian modelling with heavy-tailed
distributions has been shown to produce more reasonable conflict resolution,
typically by favouring one source of information over the other. The
less favoured source is ultimately wholly or partially rejected as the conflict
becomes increasingly extreme.
The literature reviewed here provides formal proofs of conflict resolution
by asymptotic rejection of some information sources. Results are given for
a variety of models, from the simplest case of a single observation relating
to a single location parameter up to models with many location parameters,
location and scale parameters, or other kinds of parameters. However, these
results do not begin to address models of the kind of complexity that are
routinely used in practical Bayesian modelling. In addition to reviewing the
available theory, we also identify clearly the gaps in the literature that need
to be filled in order for modellers to be able to develop applications with
appropriate “built-in robustness.” | |
| dc.description | NIH-INBRE grant P20 RR-016470 | |
| dc.language | en_US | |
| dc.publisher | Brazilian Statistical Association | |
| dc.relation | Brazilian Journal of Probability and Statistics;Vol. 26, No. 4 | |
| dc.subject | Built-in robustness | |
| dc.subject | heavy-tailed modelling | |
| dc.subject | outliers | |
| dc.subject | partial rejection of information | |
| dc.subject | rejection of information | |
| dc.subject | theory of conflict resolution | |
| dc.subject | Bayesian | |
| dc.title | Bayesian heavy-tailed models and conflict resolution: A review | |
| dc.type | Artículos de revistas | |
