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Humboldt-Universität zu Berlin - Mathematisch-Naturwissenschaftliche Fakultät - Institut für Mathematik

Forschungsseminar Mathematische Statistik

Für den Bereich Statistik

S. Greven, W. Härdle, M. Reiß, V. Spokoiny



Weierstrass-Institut für Angewandte Analysis und Stochastik
Mohrenstrasse 39
10117 Berlin



mittwochs, 10.00 - 12.00 Uhr



15. April 2020
Der Vortrag wurde aufgrund der aktuellen Lage auf den 15.07.2020 verlegt
22. April 2020
Gabriel Peyre (ENS Paris)
29. April 2020
Thorsten Dickhaus (University of Bremen)
06. Mai 2020
Julia Schaumburg(FU Amsterdam)
Dynamic clustering of multivariate panel data
Abstract: We propose a dynamic clustering model for studying time-varying group structures in multivariate panel data. The model is dynamic in three ways: First, the cluster means and covariance matrices are time-varying to track gradual changes in cluster characteristics over time. Second, the units of interest can transition between clusters over time based on a Hidden Markov model (HMM). Finally, the HMM's transition matrix can depend on lagged cluster distances as well as economic covariates. Monte Carlo experiments suggest that the units can be classified reliably in a variety of settings. An empirical study of 299 European banks between 2008Q1 and 2018Q2 suggests that banks have become less diverse over time in key characteristics. On average, approximately 3\% of banks transition each quarter. Transitions across clusters are related to cluster dissimilarity and differences in bank profitability.
13. Mai 2020
Eftychia Solea (Ruhr-Universität Bochum)
A nonparametric Graphical Model for Functional Data with Application to Brain Networks Based on fMRI
Abstract: We introduce a nonparametric graphical model whose observations on vertices are functions. Many modern applications, such as electroencephalogram and functional magnetic resonance imaging (fMRI), produce data are of this type. The model is based on additive conditional independence (ACI), a statistical relation that captures the spirit of conditional independence without resorting to multi-dimensional kernels. The random functions are assumed to reside in a Hilbert space. No distributional assumption is imposed on the random functions: instead, their statistical relations are characterized nonparametrically by a second Hilbert space, which is a reproducing kernel Hilbert space whose kernel is determined by the inner product of the first Hilbert space. A precision operator is then constructed based on the second space, which characterizes ACI, and hence also the graph. The resulting estimator is relatively easy to compute, requiring no iterative optimization or inversion of large matrices. We establish the consistency and the convergence rate of the estimator. Through simulation studies we demonstrate that the estimator performs better than the functional Gaussian graphical model when the relations among vertices are nonlinear or heteroscedastic. The method is applied to an fMRI dataset to construct brain networks for patients with attention-deficit/hyperactivity disorder.
20. Mai 2020
Chiara Amorino (Paris-Saclay)
27. Mai 2020
03. Juni 2020
Ingrid Van Keilegom (KU Leuven)
On a Semiparametric Estimation Method for AFT Mixture Cure Models
Abstract: When studying survival data in the presence of right censoring, it often happens that a certain proportion of the individuals under study do not experience the event of interest and are considered as cured. The mixture cure model is one of the common models that take this feature into account. It depends on a model for the conditional probability of being cured (called the incidence) and a model for the conditional survival function of the uncured individuals (called the latency). This work considers a logistic model for the incidence and a semiparametric accelerated failure time model for the latency part. The estimation of this model is obtained via the maximization of the semiparametric likelihood, in which the unknown error density is replaced by a kernel estimator based on the Kaplan-Meier estimator of the error distribution. Asymptotic theory for consistency and asymptotic normality of the parameter estimators is provided. Moreover, the proposed estimation method is compared with a method proposed by Lu (2010), which uses a kernel approach based on the EM algorithm to estimate the model parameters. Finally, the new method is applied to data coming from a cancer clinical trial.
10. Juni 2020
Jonathan Niles-Weed (New York University)
17. Juni 2020
Francois Bachoc (Toulouse)
24. Juni 2020
01. Juli 2020
Eric Moulines (ENS Paris)
08. Juli 2020
15. Juli 2020
Torsten Hothorn (University of Zurich)
Score-based Transformation Learning
Abstrakt: Many statistical learning algorithms can be understood as iterative procedures for explaining variation in scores, that is, in the gradient vector of some target function. The statistical interpretation of boosting as functional gradient descent is maybe the most prominent representative, but also model-based trees and forests have been discussed from this point of view. While these algorithms are agnostic with respect to the target function, we specifically discuss scores obtained from the likelihood of fully parameterised transformation models. This model class is sufficiently large and interesting while at the same time allows for a unified theoretical and computational treatment. In this line of thinking, we can understand and implement classical procedures, such as the Wilcoxon-Mann-Whitney-Rank-Sum test, the log-rank test, maximally selected rank statistics, or regression trees and contemporary statistical learning procedures, most importantly random forests and boosting, as extremes in a continuum of increasingly complex models featuring directly interpretable parameters. We discuss prognostic and predictive models of increasing complexity as transformation models for conditional distributions. The estimation of heterogeneous treatment effects from experimental and observational data is presented as one application currently receiving much interest in various disciplines.

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Frau Andrea Fiebig

Mail: fiebig@mathematik.hu-berlin.de
Telefon: +49-30-2093-45460
Fax:        +49-30-2093-45451
Humboldt-Universität zu Berlin
Institut für Mathematik
Unter den Linden 6
10099 Berlin, Germany