Research Seminar

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Autumn Semester 2015

Note: The highlighted event marks the next occurring event.

Date / Time Speaker Title Location
18 September 2015
Robin Evans
University of Oxford
Causal Models and How to Refute Them  HG G 19.1 
Abstract: Directed acyclic graph models (DAG models, also called Bayesian networks) are widely used in the context of causal inference, and they can be manipulated to represent the consequences of intervention in a causal system. However, DAGs cannot fully represent causal models with confounding; other classes of graphs, such as ancestral graphs and ADMGs, have been introduced to deal with this using additional kinds of edge, but we show that these are not sufficiently rich to capture the range of possible models. In fact, no mixed graph over the observed variables is rich enough, however many edges are used. Instead we introduce mDAGs, a class of hyper-graphs which is appropriate for representing causal models when some of the variables are unobserved. Results on the Markov equivalence of these models show that when interpreted causally, mDAGs are the minimal class of graphs which can be sensibly used. Understanding such equivalences is critical for the use of automatic causal structure learning methods, a topic in which there is considerable interest. We elucidate the state of the art as well as some open problems.
25 September 2015
Damian Kozbur
Seminar für Statistik
Testing-Based Forward Model Selection  HG G 19.1 
Abstract: This work develops theory for a Testing-Based Forward Model Selection procedure in linear regression and m-estimation problems. Forward Selection is a greedy procedure which inductively selects covariates which reliably add predictive power into a statistical model. A testing procedure, whose properties resemble traditional statistical hypothesis testing, is assumed as a primitive. It is used as a tool for guiding the forward selection by deciding which variable to include next and when to stop including variables. This offers a flexible and data- driven way to estimate models and construct predictions in high dimensional statistical problems. Probabilistic bounds for prediction error and number of selected covariates are proved for the proposed Testing- Based Forward Selection. The regularity conditions assumed on the set of hypothesis tests are verified for several example problems. The per- formance of Testing-based Forward Selection is compared to Lasso and Post-Lasso in Simulation studies.
28 September 2015
Claudia Klüppelberg
Technische Universität München
Extremes on directed acyclic graphs  HG G 19.1 
Abstract: We consider a new max-linear structural equation model, where all random variables can be written as a max-linear function of their parents and noise terms. For the corresponding graph we assume that it is a DAG. We present basic probabilistic results of our model; in particular we characterise those max-linear random vectors, which originate in a max-linear structural equation model and are, hence, max-linear distributions on DAGs. We also determine the minimal DAG corresponding to the max-linear structural equation model. As an example we consider the structural equation model with max-stable noise variables, resulting in a max-stable max-linear DAG. This is joint work with Nadine Gissibl.
23 October 2015
Alessandra Luati
University of Bologna
Generalised Linear Cepstral Models for the Spectrum of a Time Series  HG G 19.1 
Abstract: The paper introduces the class of generalised linear models with Box-Cox link for the spectrum of a time series. The Box-Cox transformation of the spectral density is represented as a finite Fourier polynomial, with coefficients, that we term generalised cepstral coefficients, providing a complete characterisation of the properties of the random process. The link function depends on a power transformation parameter and encompasses the exponential model (logarithmic link), the autoregressive model (inverse link), and the moving average model (identity link). One of the merits of this model class is the possibility of nesting alternative spectral estimation methods under the same likelihood-based framework, so that the selection of a particular parametric spectrum amounts to estimating the transformation parameter. We also show that the generalised cepstral coefficients are a one to one function of the inverse partial autocorrelations of the process, which can be used to evaluate the mutual information between the past and the future of the process.
27 October 2015
Po-Ling Loh
University of Pennsylvania
New perspectives for robust/high-dimensional estimation  HG E 41 
Abstract: Robust statistics provides a framework for quantifying the behavior of an estimator when data are subject to various imperfections which deviate from standard model assumptions. Such an understanding may aid in designing estimators that are robust to particular forms of contamination. In this talk, we will discuss two high-dimensional estimators suited for robust linear regression and precision matrix estimation. The form of the estimators is quite natural and the analysis follows from standard high-dimensional techniques; what is fascinating is the relationship between the results we derive and their analogs in low dimensions. In particular, we demonstrate that the same guiding principles for selecting estimators in classical robust statistics provide provably good behavior in high dimensions, as well.
6 November 2015
Egil Ferkingstad
NTNU Trondheim, Norway / University of Iceland
Recent progress in the R-INLA project for Bayesian computation   HG G 19.1 
Abstract: After briefly introducing the INLA approach for Bayesian computation, I will discuss two topics we're currently working on. Topic 1: A new copula-based correction ( for difficult cases, allowing us to push the boundaries of applicability of INLA while adding minimal computational cost. Topic 2: Providing sensible, default, weakly informative priors. I will present the ideas behind Penalized Complexity priors (, focusing on work in progress showing how the idea of distributing the overall variance between different model components gives a good strategy for specifying joint priors for hierarchical models.
4 December 2015
Benedikt Pötscher
University of Vienna
Finite-Sample Properties of HAC-Tests  HG G 19.1 
Abstract: Testing restrictions on regression coefficients in linear models often requires correcting the conventional F-test for potential heteroskedasticity or autocorrelation amongst the disturbances, leading to so-called heteroskedasticity and autocorrelation robust (or consistent) test procedures (HAR/HAC-tests). These procedures have been developed with the purpose of attenuating size distortions and power deficiencies present for the uncorrected F-test. We develop a general theory to establish positive as well as negative finite-sample results concerning the size and power properties of a large class of heteroskedasticity and autocorrelation robust tests. Using these results we show that nonparametrically as well as parametrically corrected F-type tests in time series regression models with stationary disturbances have either size equal to one or nuisance-infimal power equal to zero under very weak assumptions on the covariance model and under generic conditions on the design matrix. In addition we suggest an adjustment procedure based on artificial regressors. This adjustment resolves the problem in many cases in that the so-adjusted tests do not suffer from size distortions. At the same time their power function is bounded away from zero. As a second application we discuss the case of heteroskedastic disturbances.

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