[Forum SIS] Avviso di seminario :: Rue a DSS (Scienze Statistiche, Sapienza)

[Pierpaolo Brutti]

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 A v v i s o   d i   S e m i n a r i o
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Giovedì 23 Marzo, ore 11:00am
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Aula III (terzo piano)
Dipartimento di Scienze Statistiche
Sapienza Università di Roma

HÅVARD RUE
(KAUST, http://www.r-inla.org/)

terrà un seminario dal titolo

PENALISING MODEL COMPONENT COMPLEXITY: A  PRINCIPLED PRACTICAL
APPROACH TO CONSTRUCTING PRIORS

tutti gli interessati sono invitati a partecipare.

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Saluti

Pierpaolo Brutti

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ABSTRACT

Setting prior distributions on model parameters is the act of
characterising the nature of our uncertainty and has proven a critical
issue in applied Bayesian statistics. Although the prior distribution
should ideally encode the users’ uncertainty about the parameters,
this level of knowledge transfer seems to be unattainable in practice
and applied statisticians are forced to search for a “default” prior.
Despite the development of objective priors, which are only available
explicitly for a small number of highly restricted model classes, the
applied statistician has few practical guidelines to follow when
choosing the priors. An easy way out of this dilemma is to re-use
prior choices of others, with an appropriate reference.

In this talk, I will introduce a new concept for constructing prior
distributions. We exploit the natural nested structure inherent to
many model components, which defines the model component to be a
flexible extension of a base model. Proper priors are defined to
penalise the complexity induced by deviating from the simpler base
model and are formulated after the input of a user-defined scaling
parameter for that model component, both in the univariate and the
multivariate case. These priors are invariant to reparameterisations,
have a natural connection to Jeffreys’ priors, are designed to support
Occam’s razor and seem to have excellent robustness properties, all
which are highly desirable and allow us to use this approach to define
default prior distributions. Through examples and theoretical results,
we demonstrate the appropriateness of this approach and how it can be
applied in various situations, like random effect models, spline
smoothing, disease mapping, Cox proportional hazard models with
time-varying frailty, spatial Gaussian fields and multivariate probit
models, etc. Further, we show how to control the overall variance
arising from many model components in hierarchical models.

This is joint work with a lot of people related to the R-INLA project,
and is still work in progress.