Generalforsamling i DSTS d. 25/2-2014

Der afholdes generalforsamling I DSTS

25/2-2014, kl. 17.15

i lokale CSS 1.1.12, Københavns Universitets Center for Sundhed og Samfund CSS (det gamle kommunehospital, Øster Farimagsgade 5, 1014 K) .

Indkaldelse med dagsorden er vedhæftet.


Efter generalforsamlingen er der foredrag ved Steffen Lauritzen, University of Oxford



Proper scoring rules and linear estimating equations in exponential families


In models of high complexity, the computational burden involved in calculating the maximum likelihood estimator can be forbidding. Proper scoring rules (Brier 1950, Good 1952, Bregman 1967, de Finetti 1975) such as the logarithmic score, the Brier score, and others, induce natural unbiased estimating equations that generally lead to consistent estimation of unknown parameters. The logarithmic score corresponds to maximum likelihood estimation whereas a score function introduced by Hyvärinen (2005) leads to linear estimation equations for exponential families.

We shall briefly review the facts about proper scoring rules and their associated divergences, entropy measures, and estimating equations. We show how Hyvärinen’s rule leads to particularly simple estimating equations for Gaussian graphical models, including Gaussian graphical models with symmetry. 

The lecture is based on joint work with Philip Dawid, Matthew Parry, and Peter Forbes.  For a recent reference see: P. G. M. Forbes and S. Lauritzen (2013). Linear Estimating Equations for Exponential Families with Application to Gaussian Linear Concentration Models. arXiv:1311:0662


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