[Statlist] Next talks: Friday, May 23, 2014 with Gassiat Elisabeth (Université Paris-Sud) and Jane L. Hutton (University of Warwick, UK)

[Cecilia Rey]

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ETH and University of Zurich

Organisers:
Proff. P. Bühlmann - L. Held - T. Hothorn - H.R. Kuensch - M. Maathuis -
N. Meinshausen - S. van de Geer - M. Wolf

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We are glad to announce the following talks on Friday, May 23, 2014

 1) 15.15h to 16.00h   ETH Zurich HG G 19.1 with Gassiat Elisabeth (Université Paris-Sud)                                       

Titel:
Non parametric hidden Markov models

Abstract:
In this talk I will present recent results about non parametric identifiability of hidden Markov models, and some consequences in non parametric estimation. 

References: 
E. Gassiat, A. Cleynen, S. Robin 
Finite state space non parametric hidden Markov models are in general identifiable 
arxiv preprint, 2013. 

E. Gassiat, J. Rousseau 
Non parametric finite translation mixtures with dependent regime 
Bernoulli, à paraitre. 

E.Vernet 
Posterior consistency for nonparametric Hidden Markov Models with finite state space 

T. Dumont and S. Le Corff 
Nonparametric regression on hidden phi-mixing variables: identifiability and consistency of a pseudo-likelihood based estimation procedure 

	                     16.00h coffee break


 2) 16.30h to 17.150h   ETH Zurich HG G 19.1 with Jane L. Hutton (University of Warwick, UK)

Title:
Chain Event Graphs for Informative Missingness

Abstract:
Chain event graphs (CEGs) extend graphical models to address situations in which, after one variable takes a particular value, possible values of future variables differ from those following alternative values (Thwaites et al 2010). These graphs are a useful framework for modelling discrete processes which exhibit strong asymmetric dependence structures, and are 
derived from probability trees by merging the vertices in the trees 
together whose associated conditional probabilities are the same. 

We exploit this framework to develop new classes of models where 
missingness is influential and data are unlikely to be missing at random (Barclay et al 2014). Context-specific symmetries are captured by the CEG. As models can be scored efficiently and in closed form, standard Bayesian selection methods can be used to search over a range of models. 
The selected maximum a posteriori model can be easily read back to the client in a graphically transparent way. 

The efficacy of our methods are illustrated using survival of people with cerebral palsy, and a longitudinal study from birth to age 25 of children in New Zealand, analysing their hospital admissions aged 18-25 years with 
respect to family functioning, education, and substance abuse aged 16-18 years. 

P Thwaites, JQ Smith, and E Riccomagno (2010) "Causal Analysis with Chain Event Graphs" Artificial Intelligence, 174, 889-909. 

LM Barclay, JL Hutton and JQ Smith, (2014) "Chain Event Graphs for Informed Missingness", Bayesian Analysis, Vol. 9, 53-76. 

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These abstracts is also to be found under the following link: http://stat.ethz.ch/events/research_seminar
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