[Forum SIS] Annuncio di seminari - 9.12.2014

[Anna Gottard]

DiSIA  (Dipartimento di Statistica, Informatica, Applicazioni “G. Parenti”)
Università di Firenze


Martedì 9 Dicembre 2014  nell’aula 32 si terranno i seguenti seminari:


Alle ore 11.00:

Speaker: Joe Whittaker  (Lancaster University)  

Titolo: Synergy, suppression and immorality

Abstract: Give a background on suppression in regression that begins with Horst (1941), and has generated a plethora of different types. Introduce forward differences of the entropy and define synergy in terms of explained information. Characterise and generalise suppression in terms of synergy. Specialise this result to correlation matrices. Relate this to immorality via conditional synergy. Give an empirical example and some small examples from graphical models. Make some concluding remarks.


Alle ore 12.00:

Speaker: Bala Rajaratnam  (Stanford University)  

Titolo: Methods for Scalable and Robust High Dimensional Graphical Model Selection

Abstract: Learning high dimensional graphical models is a topic of contemporary interest. A popular approach is to use L1 regularization methods to induce sparsity in the inverse covariance estimator, leading to sparse partial covariance/correlation graphs. Such approaches can be grouped into two classes: (1) regularized likelihood methods and (2) regularized regression-based, or pseudo-likelihood, methods. Regression based methods have the distinct advantage that they do not explicitly assume Gaussianity. One gap in the area is that none of the popular methods proposed for solving regression based objective functions have provable convergence guarantees. Hence it is not clear if resulting estimators actually yield correct partial correlation/partial covariance graphs. To this end, we propose a new regression based graphical model selection method that is both tractable and has provable convergence guarantees. In addition we also demonstrate that our approach yields estimators that have good large sample properties. The methodology is illustrated on both real and simulated data. We also present a novel unifying framework that places various pseudo-likelihood graphical model selection methods as special cases of a more general formulation, leading to important insights. (Joint work with S. Oh and K. Khare)



Ulteriori informazioni su: http://local.disia.unifi.it/seminari.php

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Anna Gottard
Dipartimento di Statistica Informatica Applicazioni
V.le Morgagni 59, Firenze

gottard a disia.unifi.it
http://local.disia.unifi.it/gottard/
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