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 Please Note: This seminar will be given in-person.

On the second Kahn-Kalai conjecture

 Please Note: This seminar will be given in-person.

A theorem with CLT flavour, in the framework of the infinite symmetric group

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Tuning Parameter Selection for Variable Selection via R²

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Max-linear Graphical Models for Extreme Risk Modelling

Graphical models can represent multivariate distributions in an intuitive way and, hence, facilitate statistical analysis of high-dimensional data. Such models are usually modular so that high-dimensional distributions can be described and handled by careful combination of lower dimensional factors. Furthermore, graphs are natural data structures for algorithmic treatment. Moreover, graphical models can allow for causal interpretation, often provided through a recursive system on a directed acyclic graph (DAG) and the max-linear Bayesian network we introduced in [1] is a specific example. This talk contributes to the recently emerged topic of graphical models for extremes, in particular to max-linear Bayesian networks, which are max-linear graphical models on DAGs. 

In this context, the Latent River Problem has emerged as a flagship problem for causal discovery in extreme value statistics. In [2] we provide a simple and efficient algorithm QTree to solve the Latent River Problem. QTree returns a directed graph and achieves almost perfect recovery on the Upper Danube, the existing benchmark dataset, as well as on new data from the Lower Colorado River in Texas. It can handle missing data, and has an automated parameter tuning procedure. In our paper, we also show that, under a max-linear Bayesian network model for extreme values with propagating noise, the QTree algorithm returns asymptotically a.s. the correct tree. Here we use the fact that the non-noisy model has a left-sided atom for every bivariate marginal distribution, when there is a directed edge between the the nodes.

For linear graphical models, algorithms are often based on Markov properties and conditional independence properties. In [3] we characterise conditional independence properties of max-linear Bayesian networks and in my talk I will present some of these results and exemplify the difference to linear networks. 

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Considerations when Discussing Causal Concepts with Applied Researchers

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Daily air pollution and mortality

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Quantifying the risk of travelling during a pandemic using cellphone-derived mobility data

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Zero-state coupled Markov switching count models for spatio-temporal infectious disease spread

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Outcome-adaptive LASSO for confounder selection with time-varying treatments

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Forward Preferences in Insurance