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X-WR-CALNAME:Fächergruppe Mathematik und Statistik
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X-WR-CALDESC:Veranstaltungen für Fächergruppe Mathematik und Statistik
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BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20260310T140000
DTEND;TZID=Europe/Berlin:20260310T153000
DTSTAMP:20260217T074059Z
CREATED:20260127T143333Z
LAST-MODIFIED:20260217T074059Z
UID:1268-1773151200-1773156600@www.hsu-hh.de
SUMMARY:Roberto Fuentes Martínez (IMT Lucca and University of Alicante
DESCRIPTION:Granger Causality in Expectiles: a M-vine copula test\nExpectile-based Granger causality allows for a more comprehensive assessment of directional dependence\, capturing heterogeneous causal relationships across the entire distribution\, including tail regions associated with extreme risks or rare events. Furthermore\, methods grounded in copula theory provide a powerful and model-free way to describe and estimate non-linear and asymmetric dependence between variables. Consequently\, by integrating copula-based techniques into the expectile Granger causality framework\, one can more accurately model the joint distributional dynamics and uncover causal relationships that are obscured under linear and gaussian assumptions. In this work\, we introduce a model-free measure of Granger causality in expectiles that generalizes the mean-focused measure of Song and Taamouti (2018) to other parts of the distribution. Based on this measure\, we propose a Granger causality in expectiles test for $k$-Markov stationary processes based on vine copulas\, suitable for non-linear and non-gaussian dependence structures. By means of a simulation study\, we show that our test has excellent statistical properties in terms of size and power for a wide arrange of data generating processes often used for time series modeling. Lastly\, we illustrate the use our test in an empirical application with real data from financial returns.
URL:https://www.hsu-hh.de/statistik/event/roberto-fuentes-martinez-imt-lucca-and-university-of-alicante
LOCATION:Mensa Room 0001\, Deutschland
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20260224T154500
DTEND;TZID=Europe/Berlin:20260224T171500
DTSTAMP:20260225T151908Z
CREATED:20260127T141406Z
LAST-MODIFIED:20260225T151908Z
UID:1263-1771947900-1771953300@www.hsu-hh.de
SUMMARY:Andreas Löpker (HTW Dresden)
DESCRIPTION:Two Types of Time Reversals for Markov Processes\nThe talk is divided into three parts: In the first part\, the classical time reversal for stationary Markov processes is discussed for Piecewise Deterministic Markov Processes (joint work with Zbigniew Palmowski\, Wroclaw). As an example we show how one can describe the inverse of the M/G/1-workload process. The remaining two parts are based on work in progress (in the broadest sense). We look at time reversal in the non-stationary case when we specify both a fixed initial distribution and the time T from which the process is to be reversed. With two very simple examples\, an innocently looking continuous time Markov chain and the seemingly harmless Poisson process\, we demonstrate how time reversal makes sense and leads to interesting results. The last part is concerned with time reversal with fixed distributions at time 0 and at time T and whether this even makes sense. \n 
URL:https://www.hsu-hh.de/statistik/event/andreas-loepker-htw-dresden
LOCATION:Gebäude H1\, Raum 1503
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20251217T154500
DTEND;TZID=Europe/Berlin:20251217T171500
DTSTAMP:20251203T125505Z
CREATED:20251028T163052Z
LAST-MODIFIED:20251203T125505Z
UID:1245-1765986300-1765991700@www.hsu-hh.de
SUMMARY:Simon Schlumbohm (HSU)
DESCRIPTION:Efficient Algorithms for Improved Information Retention in Integration of Incomplete Omics Datasets\nThe acquisition of high-quality data in the biomedical field\, particularly in omics studies\nsuch as proteomics or transcriptomics\, poses a significant challenge due to incomplete\nmeasurements during data acquisition or simply small sample sizes. This issue results in\ndatasets with low statistical power that are in addition often compromised by missing\nvalues\, which impede downstream analysis and the accurate interpretation of biological\nphenomena. \nA common approach to mitigate such limitations is data integration\, which combines\nmultiple datasets to increase cohort sizes by incorporating data from different studies or\nlaboratories. However\, this approach introduces new challenges\, notably the so-called\nbatch effect\, which introduces internal biases and obscures biological meaning. Moreover\,\ninfrequently measured features (e.g.\, proteins or genes) create additional gaps in the data\nduring integration tasks.  \nAs the volume of available biological data continues to expand\, there is an increasing\nneed for computational methods capable of efficiently processing and analyzing these\ngrowing datasets. Expected future advancements in data acquisition with regards to\nthroughput necessitate the development of computationally efficient and robust algorithms.\nIn addition\, to ensure accessibility and broad adoption\, it is crucial that bioinformatics\ntools must be user friendly\, allowing researchers with varying levels of technical expertise\nto effectively utilize them.  \nTo this end\, an integration and batch effect reduction tool has been developed\, called\nthe HarmonizR algorithm. This work features various functionality that has been build\nto tackle the aforementioned issues. Dataset integration aims for an increase in cohort\nsizes and sample amounts\, which is facilitated by the inclusion of a new unique removal\napproach. It overcomes prior limitations regarding data retention\, greatly increasing\nHarmonizR’s benefits as a pipeline tool when used prior to data analysis by significantly\nexpanding the number of considerable features and data points of any given study. This\nmay be paired with the added functionality of accounting for user-defined experimental\ninformation such as treatment-groups (i.e.\, covariate information) during adjustment\,\nleading to more robust and high-quality results. Regarding computational efficiency\, a\nnovel blocking approach exploits the given data structure to brace the algorithm for\ncurrent and future big data challenges without negatively impacting adjustment quality.\nFurthermore\, the algorithm’s batch effect adjustment capabilities are proven effective\non various omics types – with a notable extension towards single cell count datasets by\nemploying further adjustment methodology – as well as non-biological data in the form of\nan attention-deficit/hyperactivity disorder study.  \nTo address remaining challenges\, the newly developed BERT algorithm introduces a novel\narchitectural approach\, offering improvements in information retention and computational\nefficiency. A comparative analysis of BERT and HarmonizR explores the advantages\nof BERT in terms of feature/overall data retention and reduced runtimes\, providing a\nvaluable complement to the existing framework.  \nLastly\, to enhance accessibility and ease of use\, plugins for the popular Perseus software\nhave been created and are described\, enabling seamless integration of both algorithms\ninto established bioinformatics workflows\, specifically aiding researchers less familiar with\nthe technical aspects of the here shown algorithms and bioinformatics in general.
URL:https://www.hsu-hh.de/statistik/event/simon-schlumbohm-hsu
LOCATION:Gebäude H1\, Raum 1503
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20251208T154500
DTEND;TZID=Europe/Berlin:20251208T171500
DTSTAMP:20251126T135247Z
CREATED:20251028T162731Z
LAST-MODIFIED:20251126T135247Z
UID:1241-1765208700-1765214100@www.hsu-hh.de
SUMMARY:Gaby Schneider (Goethe-U Frankfurt/Main)
DESCRIPTION:Bivariate change point detection in cell biology and neuroscience\n(Moving kernel statistics for change point detection in cell biology and neuroscience)\nNeuronal spike trains show a diversity of patterns\, including short- and long-term changes in their intensity or regularity of spike events. To analyze their impact on information processing\, point process models are needed that capture these patterns\, and techniques for the localization of change points on different time scales are required. We discuss a class of point process models that exhibit changes in the intensity and variance of life times. A multi filter procedure is proposed to test the null hypothesis of constant intensity or variance. Change points are then located with a two step procedure in which changes in the intensity are located first and then incorporated to investigate changes in the regularity. \nIn cell biology\, similar questions arise when movement patterns are quasi linear with abrupt changes in direction and speed\, as in movements of plastids investigated here. We first propose a new stochastic model called linear walk that describes movement along linear structures with piecewise constant movement direction and speed. Maximum likelihood estimators are provided\, and due to serial dependence of increments\, the classical MOSUM statistic is replaced by a moving kernel estimator. Convergence of the resulting difference process and strong consistency of the variance estimator are shown. We estimate the change points and propose a graphical technique to distinguish between change points in movement direction and speed. \nThis talk is based on joint work with Stefan Albert\, Theresa Ernst\, Philipp Gebhardt\, Michael Messer\, Annika Meyer\, Ralph Neininger\, Solveig Plomer\, Jochen Roeper\, Julia Schiemann and Enrico Schleiff.
URL:https://www.hsu-hh.de/statistik/event/gaby-schneider-goethe-u-frankfurt-main
LOCATION:Gebäude H1\, Raum 1503
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20250624T154500
DTEND;TZID=Europe/Berlin:20250624T171500
DTSTAMP:20250429T070225Z
CREATED:20250429T065942Z
LAST-MODIFIED:20250429T070225Z
UID:1215-1750779900-1750785300@www.hsu-hh.de
SUMMARY:Renate Tobies (Uni Jena)
DESCRIPTION:Die Techno- und Wirtschaftsmathematikerin Iris Runge (1888-1966):\nIhr Weg in die und in der Industrieforschung (Osram und Telefunken)\nIris Runge\, älteste Tochter des Numerikers Carl Runge (1856-1927)\, studierte (Ma\, Ph\, Erdk.\, Ch) in Göttingen und ein Semester in München\, promovierte mit math. Methoden in Physikalischer Chemie (Göttingen 1922). Sie wurde bereits als Studentin in Projekte einbezogen und publizierte mit Arnold Sommerfeld in München ihre erste Arbeit. Nach einer Tätigkeit als Lehrerin bewarb sie sich 1923 bei Osram\, wo sie zur mathematischen Beraterin für ein breites Spektrum von Themenfeldern in der Glühlampen- und Elektronenröhrenforschung avancierte: math. Statistik (Qualitätskontrolle); Optik\, Farbmetrik; Materialforschung. Sie publizierte zahlreiche Forschungsberichte\, Artikel in Fachzeitschriften und war maßgebliche Autorin des ersten Buches Anwendungen der mathematischen Statistik auf Probleme der Massenfabrikation (Julius Springer\, 1927\, 2. Aufl. 1930).
URL:https://www.hsu-hh.de/statistik/event/dr-habil-renate-tobies
LOCATION:Hörsaal 1\, HSU Gebäude H1\, Hamburg\, Germany
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20241128T161500
DTEND;TZID=Europe/Berlin:20241128T174500
DTSTAMP:20241030T081751Z
CREATED:20241030T081750Z
LAST-MODIFIED:20241030T081751Z
UID:1197-1732810500-1732815900@www.hsu-hh.de
SUMMARY:Fabian Scheipl (LMU München)
DESCRIPTION:tidyfun: Tidy Exploratory Analysis for Functional Data\nR packages „tf“ and „tidyfun“ implement a unified interface for working with regularly or irregularly observed function-valued data. The packages follow the tidyverse design philosophy of R packages and are aimed at lowering the barrier of entry for analysts in order to quickly and painlessly analyse and interact with functional data\, specifically in datasets that contain both scalar and functional data or multiple types of functional data measured over different domains.\nWe discuss the available feature set as well as extensions currently under development and show some simple application examples.
URL:https://www.hsu-hh.de/statistik/event/fabian-scheipl-lmu-muenchen
LOCATION:Mensa Room 0001\, Deutschland
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20240522T154500
DTEND;TZID=Europe/Berlin:20240522T171500
DTSTAMP:20240327T115522Z
CREATED:20240327T115522Z
LAST-MODIFIED:20240327T115522Z
UID:1171-1716392700-1716398100@www.hsu-hh.de
SUMMARY:Marc-Oliver Pohle  HITS Heidelberg
DESCRIPTION:Generalised Covariance and Correlations\nThe covariance of two random variables measures the average joint deviations from their respective means. We generalise this well-known measure by replacing the means with other statistical functionals such as quantiles\, expectiles\, or thresholds. Deviations from these functionals are defined via generalised errors\, often induced by identification or moment functions. As a normalised measure of dependence\, a generalised correlation is constructed. Replacing the common Cauchy-Schwarz normalisation by a novel Fréchet-Hoeffding normalisation\, we obtain attainability of the entire interval [−1\,1] for any given marginals. We uncover favourable properties of these new dependence measures. The families of quantile and threshold correlations give rise to function-valued distributional correlations\, exhibiting the entire dependence structure. They lead to tail correlations\, which should arguably supersede the coefficients of tail dependence. Finally\, we construct summary covariances (correlations)\, which arise as (normalised) weighted averages of distributional covariances. We retrieve Pearson covariance and Spearman correlation as special cases. The applicability and usefulness of our new dependence measures is illustrated on demographic data from the Panel Study of Income Dynamics.
URL:https://www.hsu-hh.de/statistik/event/marc-oliver-pohle-hits-heidelberg
LOCATION:Gebäude H1\, Raum 1503
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20240319T154500
DTEND;TZID=Europe/Berlin:20240319T171500
DTSTAMP:20240129T085803Z
CREATED:20240124T093954Z
LAST-MODIFIED:20240129T085803Z
UID:1151-1710863100-1710868500@www.hsu-hh.de
SUMMARY:Annette Möller (Universität Bielefeld)
DESCRIPTION:Vine Copula based Probabilistic Weather Forecasting \n… text follows soon …
URL:https://www.hsu-hh.de/statistik/event/annette-moeller-universitaet-bielefeld
LOCATION:Gebäude H1\, Raum 1503
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20240305T154500
DTEND;TZID=Europe/Berlin:20240305T171500
DTSTAMP:20240124T094227Z
CREATED:20240110T132902Z
LAST-MODIFIED:20240124T094227Z
UID:1133-1709653500-1709658900@www.hsu-hh.de
SUMMARY:Yannis Schumann (HSU)
DESCRIPTION:Molecular Classification of Ependymomas Using Histological Images and Deep Neural Networks \nEpendymomas represent a rare type of tumor in the central nervous system that affects both\nchildren and adults. For these tumors\, strong differences in quality of diagnostic healthcare exist\nbetween medical centers in Germany. Thus\, molecular analyses (e.g.\, DNA methylation profiling)\nare increasingly used to validate the pathological diagnoses from traditional examination of\nhistological images. However\, these molecular analyses are expensive and are not readily available\nworldwide. Thus\, we employ deep neural networks to predict the molecular properties of\nependymomas from large-scale\, histological image data and aim to support neuropathologists in the\nintegrated diagnosis of this challenging tumor entity. \nKey findings: \n\nUsing histological image data\, the molecular (DNA methylation) type can be accurately predicted for ependymomas from all major anatomical compartments\nColor normalization\, color augmentation and the choice of multiple-instance pooling operation are major factors that determine domain-adaptation to other imaging facilities\nSimple code optimization steps facilitate highly efficient data processing on the supercomputer HSUper
URL:https://www.hsu-hh.de/statistik/event/yannis-schumann-hsu
LOCATION:Gebäude H1\, Raum 1503
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20240227T154500
DTEND;TZID=Europe/Berlin:20240227T171500
DTSTAMP:20240124T094235Z
CREATED:20240124T092948Z
LAST-MODIFIED:20240124T094235Z
UID:1145-1709048700-1709054100@www.hsu-hh.de
SUMMARY:Leonie Selk (Universität Hamburg)
DESCRIPTION:Variable selection in nonparametric regression with functional covariates \nWe consider a nonparametric regression model with multiple functional covariates\, allowing for additional covariates of other types (categorical\, continuous). The estimation method is based on an extension of the Nadaraya-Watson estimator\, where a kernel function is applied to a linear combination of distance measures\, each computed on individual covariates. We are interested in distinguishing between relevant covariates and noise variables. It can be shown that a data-driven least squares cross-validation method can asymptotically remove irrelevant noise variables. Based on this understanding\, a thresholded version of the extended Nadaraya-Watson estimator is proposed to perform variable selection.
URL:https://www.hsu-hh.de/statistik/event/leonie-selk-universitaet-hamburg
LOCATION:Gebäude H1\, Raum 1503
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20240116T154500
DTEND;TZID=Europe/Berlin:20240116T171500
DTSTAMP:20231206T161610Z
CREATED:20231206T161437Z
LAST-MODIFIED:20231206T161610Z
UID:1117-1705419900-1705425300@www.hsu-hh.de
SUMMARY:Andreas Galka (WTD 71 Kiel)
DESCRIPTION:Analysis of Hydroacoustical Time Series by State Space Modelling\nThis talk deals with predictive time-domain modelling of time series\, either univariate or multivariate. The aim is to identify and reconstruct independent source components from the time series. For this purpose\, a special class of linear state space models is employed\, which describes the individual source components by autoregressive moving-average (ARMA) processes. As a useful result of ARMA modelling\, parametric estimates of the power spectra of the source components can be obtained. In a simulation study\, it is demonstrated that also components generated by nonlinear processes can be separated by linear state space modelling\, using Kalman filtering and smoothing. Furthermore\, comparison with algorithms from Independent Component Analysis (ICA) shows that ICA fails to distinguish correlations due to mixing from correlations due to finite data set size\, leading to distorted estimates. Parameter estimation is performed by numerical maximisation of the logarithmic likelihood\, using the Expectation Maximisation (EM) algorithm and other algorithms for optimisation. The implementation of the EM algorithm for state space modelling under constraints is discussed in detail\, and some new results are presented. Finally\, results for applying state space modelling to several hydroacoustic time series are presented\, including components of the underwater signature of ships and vocalisations of marine mammals. Special emphasis is given to phenomena typical of ship components\, such as frequency combs and Doppler effects.
URL:https://www.hsu-hh.de/statistik/event/andreas-galka-wtd-71-kiel
LOCATION:Gebäude H1\, Raum 1503
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20240109T154500
DTEND;TZID=Europe/Berlin:20240109T171500
DTSTAMP:20231219T131625Z
CREATED:20231219T131546Z
LAST-MODIFIED:20231219T131625Z
UID:1125-1704815100-1704820500@www.hsu-hh.de
SUMMARY:Christian Weiß (HSU)
DESCRIPTION:Testing for Dependence by Using Ordinal Patterns: an Introduction\nAbout 20 years ago\, ordinal patterns have been introduced as a simple\, robust\, and flexible tool for analyzing the serial dependence structure of univariate real-valued stochastic processes. If applied to continuously distributed processes\, one can derive non-parametric tests of the null hypothesis that the process is independent and identically distributed. Recently\, also more sophisticated tasks for dependence tests have been considered: serial dependence in a discrete-valued process\, the sequential monitoring of serial dependence\, cross-dependence in a multivariate process\, and spatial dependence in a random field. This talk provides an introduction to the aforementioned topics together with illustrative examples\, and it concludes by outlining perspectives for future research.
URL:https://www.hsu-hh.de/statistik/event/christian-weiss-hsu-2024
LOCATION:Gebäude H1\, Raum 1503
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20231219T154500
DTEND;TZID=Europe/Berlin:20231219T171500
DTSTAMP:20231206T161553Z
CREATED:20231206T155926Z
LAST-MODIFIED:20231206T161553Z
UID:1108-1703000700-1703006100@www.hsu-hh.de
SUMMARY:Russell Shinohara (University of Pennsylvania)
DESCRIPTION:Statistical Approaches to Harmonization in Multi-Center Medical Imaging Studies\nWhile magnetic resonance imaging (MRI) studies are critical for the diagnosis\, monitoring\, and study of a wide variety of diseases\, their use in quantitative analysis can be complex. An increasingly recognized issue involves the differences between MRI scanners that are used in large multi-center studies. To address this\, the current state of the art is to „regress out“ or „adjust for“ scanner differences. The field has found these methods to be insufficient and has advocated for the adaptation of methods pioneered in genomics to help mitigate inter-scanner differences\, which can vary across the brain and result in both mean and variance shifts. We further study the implications of differences in correlation structures across and between images\, and how this affects downstream inference.
URL:https://www.hsu-hh.de/statistik/event/russell-shinohara-university-of-pennsylvania
LOCATION:Gebäude H1\, Raum 1503
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20231212T154500
DTEND;TZID=Europe/Berlin:20231212T171500
DTSTAMP:20231123T091604Z
CREATED:20231123T091604Z
LAST-MODIFIED:20231123T091604Z
UID:1100-1702395900-1702401300@www.hsu-hh.de
SUMMARY:Paul Doukhan (Université Cergy Paris)
DESCRIPTION:Dependence\, examples and tools\nThe talk first aims at recalling some basic facts linking independence and orthogonality structures. This will mange to provide simple conditions to model stochastic dependences. Some other simple features will also suggest a definition of weak proposed in a paper of 1999 with Sana Louhichi which has some advantages with respect to the more classical strong mixing ones as Rosenblatt’s 1956 proposed. Definitely I don’t think that only such conditions have to be used\, since developing some examples\, many other notions of dependence should in fact be used rather than only one. The main focus is definitely the class of models of time series of interest. A kind of botanic description will make it clear that from the point of view of examples\, such weak dependence conditions (also developed in a LNS volume (with other coauthors) admits some nice features as well a a reasonable limit theory. Especially the case of integer valued models of time series has some good connection to weak dependence.
URL:https://www.hsu-hh.de/statistik/event/paul-doukhan-universite-cergy-paris
LOCATION:Gebäude H1\, Raum 1503
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20231017T154500
DTEND;TZID=Europe/Berlin:20231017T171500
DTSTAMP:20230919T125843Z
CREATED:20230809T154235Z
LAST-MODIFIED:20230919T125843Z
UID:1081-1697557500-1697562900@www.hsu-hh.de
SUMMARY:Maxime Faymonville (TU Dortmund)
DESCRIPTION:Goodness-of-fit testing for INAR models\nIn recent years\, there has been a growing interest in the analysis of time series of counts. Among the various models designed for dependent count data\, integer-valued autoregressive (INAR) processes enjoy great popularity. These processes serve as a natural extension of the widely known AR model used in the context of continuous autoregressive time series and have been used extensively in the statistical literature. Typically\, statistical inference for INAR models relies on asymptotic theory and tends to rest upon rather stringent (parametric) model assumptions. Notably\, the Poisson-INAR(1) model\, a prominent example\, has received considerable attention in existing literature. We present a novel semiparametric goodness-of-fit test tailored for the INAR model class\, without imposing any parametric assumptions on the distribution of innovations. While parametric assumptions streamline the approach and other straightforward testing strategies\, they often introduce too restrictive model assumptions. Our proposed procedure relies on the specific structure of the joint probability generating function of INAR models. This approach allows for enhancing the versatility and applicability of INAR models by accommodating a broader array of innovation distributions. We prove the validity of our testing procedure and carefully examine its performance characteristics\, including power and size\, through diverse simulation scenarios.
URL:https://www.hsu-hh.de/statistik/event/maxime-faymonville-tu-dortmund
LOCATION:Gebäude H1\, Raum 1503
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20231010T154500
DTEND;TZID=Europe/Berlin:20231010T171500
DTSTAMP:20230919T125721Z
CREATED:20230809T153614Z
LAST-MODIFIED:20230919T125721Z
UID:1076-1696952700-1696958100@www.hsu-hh.de
SUMMARY:Angelika Silbernagel (Uni Siegen)
DESCRIPTION:Ordinal patterns: Different representations and their application in the context of dependence\nSince the seminal paper by Bandt and Pompe\, so called ordinal patterns have been used extensively in contexts of data analysis\, dynamical systems as well as time series analysis and mathematical statistics. Ordinal patterns are defined as the description of the order of the values in a data set (vector) of length d. Due to this simple definition\, there are various ways on how to encode ordinal patterns. Sometimes authors pick a certain representation\, telling their readers\, why it is useful in the context they have in mind. Most of the time\, however\, one gets the impression\, that the representation was chosen randomly or only because ‘others have used it before’. \nTherefore\, here we describe and analyze different approaches to represent ordinal patterns. All of these can be found in the literature. The most important representations (plus sub-classes) are compared in terms of their applicability in different contexts\, namely\, we consider digital implementation\, inverse patterns and ties between values. Thereafter\, we consider a simulation study with regard to dependence between time series using so called multivariate ordinal patterns in order to demonstrate the use of ordinal pattern representations in practice. At the end we provide a guideline on which occasions which representation should be used.
URL:https://www.hsu-hh.de/statistik/event/angelika-silbernagel-uni-siegen
LOCATION:Gebäude H1\, Raum 1503
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20230621T154500
DTEND;TZID=Europe/Berlin:20230621T171500
DTSTAMP:20230522T082303Z
CREATED:20230522T082303Z
LAST-MODIFIED:20230522T082303Z
UID:1059-1687362300-1687367700@www.hsu-hh.de
SUMMARY:Hakam Kondakji (HSU)
DESCRIPTION:Optimale Portfolios in einem Finanzmarkt mit Gaußscher Drift und Expertenmeinungen\nWir untersuchen optimale Portfoliostrategien für nutzenmaximierende Investoren in einem zeitstetigen Finanzmarktmodell\, bei dem die Driftdurch einen Ornstein-Uhlenbeck-Prozess modelliert wird\, welcher nicht direkt beobachtbar und vom Investor aus den ihm zur Verfügung stehenden Information zu schätzen ist. In der Praxis beziehen Investoren für die Bestimmung ihrer Anlagestrategien auch andere externe Informationsquellen ein\, um die Einschätzung der unbekannten Drift zu verbessern. Dabei handelt es sich z.B. um Wirtschaftsnachrichten\, Unternehmensberichte\, Ratings\, Empfehlungen von Finanzanalysten oder die eigene individuelle Sicht auf die zukünftige Preisentwicklung. Solche externen Informationen werden in der Literatur Expertenmeinungen genannt.
URL:https://www.hsu-hh.de/statistik/event/hakam-kondakji-hsu
LOCATION:Gebäude H1\, Raum 1503
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20230614T154500
DTEND;TZID=Europe/Berlin:20230614T171500
DTSTAMP:20230512T094705Z
CREATED:20230512T094704Z
LAST-MODIFIED:20230512T094705Z
UID:1051-1686757500-1686762900@www.hsu-hh.de
SUMMARY:Christian Weiß (HSU) 2023
DESCRIPTION:Über „magische Steine“ in der Statistik\nAuf Charles Stein geht die Idee zurück\, parametrische Verteilungsfamilien auf eindeutige Weise durch eine auf Momenten basierende Identität zu charakterisieren\, wobei eine solche Identität von einer unbestimmten Funktion f abhängt\, welche aus einer großen Klasse von Funktionen frei wählbar ist. Ursprünglich wurde dieser Ansatz dabei im Rahmen der Wahrscheinlichkeitstheorie entwickelt\, um den Fehler bei Verteilungsapproximationen abzuschätzen. Vor einigen Jahren\, als es um die Vorbereitung des späteren DFG-Projekts „Modelldiagnostik für Zähldatenzeitreihen“ ging\, siehe  \nModelldiagnostik für Zähldatenzeitreihen \n \nhatte ich die Idee\, dass man auf Basis einer solchen Stein-Identität vielleicht auch einen einfachen aber flexiblen Goodness-of-Fit-Test (GoF-Test) kreieren könnte\, der bei passender Wahl der o.g. Funktion f für verschiedenste Alternativszenarien trennscharf sein könnte. Was ich ursprünglich nur als „nette kleine Idee“ unter vielen anderen Möglichkeiten eingeschätzt hatte\, hat sich mittlerweile als eine wahre Wundertüte („It’s magic!“) mit diversen (insbesondere statistischen) Anwendungsmöglichkeiten herausgestellt\, weshalb der Titel dieses Vortrags etwas flapsig von „magischen Steinen“ spricht. Im (hoffentlich) kurzweiligen Vortrag selbst soll es um einige solcher Möglichkeiten gehen\, nämlich jene\, deren ich mir bis dato bewusst geworden bin. Beginnend mit der noch wahrscheinlichkeitstheoretischen Anwendung der Berechnung auch komplizierter Momente\, sollen anschließend drei statistische Anwendungsgebiete kurz vorgestellt werden: GoF-Tests für parametrische Verteilungsfamilien\, Kontrollkarten auf Basis von Stein-Identitäten\, und eine ebenfalls auf Stein-Identitäten basierende verallgemeinerte Momentenschätzung. Die dabei präsentierten Resultate sind teils publiziert\, teils „work in progress“\, und zumeist mit verschiedensten Coautoren entstanden\, insbesondere mit Boris Aleksandrov und Simon Nik.
URL:https://www.hsu-hh.de/statistik/event/christian-weiss-hsu-2023
LOCATION:Gebäude H1\, Raum 1503
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20230524T154500
DTEND;TZID=Europe/Berlin:20230524T171500
DTSTAMP:20230512T095942Z
CREATED:20230512T093801Z
LAST-MODIFIED:20230512T095942Z
UID:1042-1684943100-1684948500@www.hsu-hh.de
SUMMARY:Philipp Otto (Uni Hannover)
DESCRIPTION:Statistical process monitoring of artificial neural networks\nThe rapid advancement of models based on artificial intelligence demands innovative monitoring techniques which can operate in real time with low computational costs. In machine learning\, especially if we consider artificial neural networks (ANN)\, the models are often trained in a supervised manner. Consequently\, the learned relationship between the input and the output must remain valid during the model’s deployment. If this stationarity assumption holds\, we can conclude that the ANN generate accurate predictions. Otherwise\, the retraining or rebuilding of the model is required. We propose considering the latent feature representation of the data (called „embedding“) generated by the ANN to determine the time when the data stream starts being nonstationary. In particular\, we monitor embeddings by applying multivariate control charts based on the data depth calculation and normalized ranks. The performance of the introduced method is evaluated by designing a benchmark study with various ANN architectures and different underlying data formats.
URL:https://www.hsu-hh.de/statistik/event/philipp-otto-uni-hannover
LOCATION:Gebäude H1\, Raum 1503
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20230517T154500
DTEND;TZID=Europe/Berlin:20230517T171500
DTSTAMP:20230508T075818Z
CREATED:20230505T165623Z
LAST-MODIFIED:20230508T075818Z
UID:1022-1684338300-1684343700@www.hsu-hh.de
SUMMARY:Malte Jahn (HSU)
DESCRIPTION:Regressing on distributions in panel models: The nonlinear effect of temperature on regional economic growth\nA framework is proposed for the situation where certain explanatory variables are available at a higher temporal resolution than the dependent variable. The main idea is to use the moments of the empirical distribution of these variables to construct regressors with the correct resolution. As the moments are likely to display nonlinear marginal and interaction effects\, an artificial neural network regression function is proposed. The usefulness is demonstrated by analyzing the influence of daily temperatures in 260 European NUTS2 regions on the yearly growth of gross value added in these regions in the time period 2000 to 2021. In the particular example\, the model allows for an appropriate assessment of regional economic impacts resulting from (future) changes in the regional temperature distribution (mean AND variance).
URL:https://www.hsu-hh.de/statistik/event/malte-jahn-hsu
LOCATION:Gebäude H1\, Raum 1503
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20230426T154500
DTEND;TZID=Europe/Berlin:20230426T171500
DTSTAMP:20230505T165601Z
CREATED:20230505T165529Z
LAST-MODIFIED:20230505T165601Z
UID:1025-1682523900-1682529300@www.hsu-hh.de
SUMMARY:Carina Beering (HSU)
DESCRIPTION:Under weak moment conditions\, we provide a functional central limit theorem (FCLT) for weighted sums of locally stationary processes for two main frameworks which differ in terms of the boundedness of the used function. Since the FCLT itself insinuates beneficial effects of a bootstrap analogue\, we transfer our previous results to the bootstrap world using a local block bootstrap approach. Afterwards\, we combine real- and bootstrap-world findings to present a testing procedure for independence of locally stationary processes using a weighted distance composed of characteristic functions and its empirical version as a base. In the end\, we illustrate the functionality of our test regarding underlying independence as well as dependence of different forms with a simulation study.
URL:https://www.hsu-hh.de/statistik/event/carina-beering-hsu-2
LOCATION:Gebäude H1\, Raum 1503
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20221012T160000
DTEND;TZID=Europe/Berlin:20221012T170000
DTSTAMP:20221005T103504Z
CREATED:20221005T103223Z
LAST-MODIFIED:20221005T103504Z
UID:975-1665590400-1665594000@www.hsu-hh.de
SUMMARY:Ángel López-Oriona (University of A Coruña\, Spain)
DESCRIPTION:Clustering of categorical time series based on two novel feature-based distances with an application to biological sequences\nTwo novel distances between categorical time series are introduced. Both of them measure discrepancy between extracted features describing the underlying serial dependence patterns. One of them is based on well-known association measures. The other relies on the so-called binarization of a categorical process\, which indicates the presence of each category by means of a canonical vector. The binarization is used to construct a collection of innovative association measures capturing every possible type of serial dependence. The metrics are used to construct crisp and fuzzy algorithms for clustering nominal series. The proposed approaches are able to group together series generated from similar underlying stochastic processes\, achieve accurate results with series coming from a broad range of models and are computationally efficient. Extensive simulation studies show that both hard and soft clustering algorithms outperform several alternative procedures presented in the literature. Two applications involving biological sequences from different species highlight the usefulness of the introduced techniques.
URL:https://www.hsu-hh.de/statistik/event/angel-lopez-oriona
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20210414T180000
DTEND;TZID=Europe/Berlin:20210414T190000
DTSTAMP:20210325T135704Z
CREATED:20210325T135552Z
LAST-MODIFIED:20210325T135704Z
UID:952-1618423200-1618426800@www.hsu-hh.de
SUMMARY:Christian Weiß (HSU)
DESCRIPTION:Soft-clipping INGARCH Models for Time Series of Bounded Counts\nThe soft-clipping binomial INGARCH (scBINGARCH) models are proposed as nearly linear time series models for bounded counts with possibly negative autocorrelations. Conditions that guarantee the existence and certain mixing properties of the scBINGARCH process are derived\, and further stochastic properties are discussed. The consistency and asymptotic normality of maximum likelihood estimators are established\, and finite-sample properties are studied with simulations. The practical relevance of the scBINGARCH’s ability to allow for negative parameter and ACF values is demonstrated by some real-data examples.
URL:https://www.hsu-hh.de/statistik/event/christian-weiss-hsu
LOCATION:BBB
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20200318T154500
DTEND;TZID=Europe/Berlin:20200318T171500
DTSTAMP:20200129T094942Z
CREATED:20200129T094804Z
LAST-MODIFIED:20200129T094942Z
UID:871-1584546300-1584551700@www.hsu-hh.de
SUMMARY:Yves Breitmoser (Uni Bielefeld)
DESCRIPTION:An axiomatic foundation of conditional logit\nThis paper considers a decision maker choosing from a set of options when options have multiple real-valued attributes. Assuming DM chooses all options with positive probability\, four invariance assumptions are necessary and sufficient for choice\nprobabilities to take McFadden’s conditional logit form: independence of irrelevant alternatives\, translation invariance\, presentation independence and context independence. Variations on these assumptions yield generalized logit and contextual logit\nmodels. This shows that even specific logit models have behavioral foundations in simple invariance assumptions involving observables only\, which therefore are\ndirectly testable.
URL:https://www.hsu-hh.de/statistik/event/an-axiomatic-foundation-of-conditional-logit
LOCATION:Gebäude H1\, Raum 1503
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20200304T154500
DTEND;TZID=Europe/Berlin:20200304T171500
DTSTAMP:20200129T120142Z
CREATED:20200129T093447Z
LAST-MODIFIED:20200129T120142Z
UID:862-1583336700-1583342100@www.hsu-hh.de
SUMMARY:Annette Möller (TU Clausthal)
DESCRIPTION:Vine copula based post-processing of ensemble forecasts for temperature\nTo account for forecast uncertainty in numerical weather prediction (NWP) models it has become common practice to employ ensemble prediction systems generating probabilistic forecast ensembles by multiple runs of the NWP\nmodel\, each time with variations in the details of the numerical model and/or initial and boundary conditions. However\, forecast ensembles typically exhibit biases and dispersion errors as they are not able to fully represent uncertainty in NWP models. Therefore\, statistical postprocessing models are employed to correct ensembles for biases and dispersion errors in conjunction with recently observed forecast errors. We propose a novel postprocessing approach for temperature forecasts based on D-vine copula quantile regression. It is a multivariate regression approach predicting quantiles of the response (temperature observations) conditioned on a set of predictor variables (the ensemble forecasts)\, while not making specific assumptions about the shape of the conditional quantiles. It exploits the dependence between observation and predictors\, accounting for non-gaussian dependencies in a flexible and data driven way. In a comparative study with temperature forecasts of different forecast horizons from the European Center for Medium Range Weather Forecast (ECMWF) the D-vine postprocessing approach shows to be highly competitive to the state-of-the-art EMOS model\, improving over standard EMOS especially for larger forecast horizons. Furthermore\, an exploratory data analysis revealed that the dependency between temperature observations and its ensemble forecasts is indeed non-Gaussian\, pointing to the need to employ vine copula models for postprocessing\, as they allow for more flexibility in the dependence structure than state-of-the-art models.
URL:https://www.hsu-hh.de/statistik/event/vine-copula-based-post-processing
LOCATION:Gebäude H1\, Raum 1503
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20200129T154500
DTEND;TZID=Europe/Berlin:20200129T171500
DTSTAMP:20200129T115746Z
CREATED:20191008T114046Z
LAST-MODIFIED:20200129T115746Z
UID:811-1580312700-1580318100@www.hsu-hh.de
SUMMARY:Houssem Brairi (USTHB Algerien)
DESCRIPTION:Testing discrete-valued time series for whiteness\nWe consider the problem of testing a univariate discrete-valued time series for whiteness in the sequency domain\, using Walsh–Fourier analysis. We show that the distribution of the lag window estimator of the Walsh spectral density is a scaled chi-square distribution\, where the scale and degrees of freedom\, both depend on the bandwidth of the smoothing window associated with the estimator. The definition of the bandwidth is extended from the frequency to the sequency domain. To address our problem\, we propose three tests: the first one is based on the cumulative Walsh periodogram\, and is shown to converge to a Brownian bridge. The second test is based on applying the Cramer–von Mises functional to an estimate of the Walsh spectral density\, and is shown to converge to a Normal distribution\, while the last test is based on a distance to whiteness\, and is shown to have an approximate scaled chi-square distribution. Simulations are reported on the performance of the tests. Finally\, we apply the proposed tests to the brain functional connectivity of schizophrenic patients.
URL:https://www.hsu-hh.de/statistik/event/testing-discrete-valued-time-series-for-whiteness
LOCATION:Gebäude H1\, Raum 1503
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20200115T080000
DTEND;TZID=Europe/Berlin:20200115T170000
DTSTAMP:20200115T172147Z
CREATED:20200115T172146Z
LAST-MODIFIED:20200115T172147Z
UID:856-1579075200-1579107600@www.hsu-hh.de
SUMMARY:Marco Meyer (TU Braunschweig)
DESCRIPTION:Extrapolation of GIDAS accident data to Germany and Europe\nWe investigate traffic accident data from project GIDAS (German In-Depth Accident Study). This project collects detailed accident data in two reference regions within Germany\, Hanover and Dresden. For each accident with severely injured persons in these regions\, the regular accident recording by police and medical staff is accompanied on site by a GIDAS team consisting of automotive engineers and accident researchers. The result is a very detailed data set that is\, however\, restricted to two particular regions. We develop methodology to extrapolate the influence of certain factors on the distribution of injury severity to other regions in Germany and Europe. The main problem is to take into account structural differences between the regions\, such as relation between traffic on highways/country roads/inner-city roads\, different vehicle types and so on. In particular\, we have to compare discrete distributions over pre-defined classes of injury severity that may vary only by small amounts across regions.
URL:https://www.hsu-hh.de/statistik/event/extrapolation-of-gidas-accident
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20191002T161500
DTEND;TZID=Europe/Berlin:20191002T174500
DTSTAMP:20190927T062601Z
CREATED:20190911T163844Z
LAST-MODIFIED:20190927T062601Z
UID:800-1570032900-1570038300@www.hsu-hh.de
SUMMARY:Alexander Schnurr (Uni Siegen)
DESCRIPTION:Ordinal Patterns and Ordinal Pattern Dependence\nOrdinal patterns describe the order structure of data points over a small time horizon. Using a moving window approach we reduce the complexity of a time series by analyzing the sequence of ordinal patterns instead of the original data. We present limit theorems for ordinal pattern probabilities and tests for structural breaks in the short-range dependent as well as in the long-range dependent setting. In the long-range dependent case\, we investigate the ordinal information of a subordinated Gaussian process with a non-summable autocovariance function. We establish the asymptotic behavior of different estimators for ordinal pattern probabilities by using a multivariate Hermite decomposition. \nOrdinal pattern dependence is a new way of measuring the degree of dependence between time series. Since it only relies on the ordinal structure of the data\, it is robust against monotone transformations and measurement errors. This method has proved to be useful already in the context of hydrological\, financial as well as medical data. Using this concept it is possible to analyze whether the dependence structure between two time series changes over time.
URL:https://www.hsu-hh.de/statistik/event/ordinal-patterns-and-ordinal
LOCATION:Gebäude H1\, Raum 1503
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20190820T154500
DTEND;TZID=Europe/Berlin:20190820T154500
DTSTAMP:20190716T115847Z
CREATED:20190703T195914Z
LAST-MODIFIED:20190716T115847Z
UID:790-1566315900-1566315900@www.hsu-hh.de
SUMMARY:Burcu Aytacoglu (Ege University)
DESCRIPTION:Effect of estimation under non-normality on the phase II performance of linear profile monitoring approaches\nRecently\, there have been several studies about control charts to monitor profiles\, where the quality of a process/product is expressed as function of response and explanatory variable(s). Mostly\, it is assumed that the in-control parameter values are known and the error terms are normally distributed. However\, these assumptions are rarely satisfied in practice. In this study\, we focused on three popular methods (EWMA-R\, EWMA-3\, and EWMA3(d2)) for monitoring simple linear profiles and the performance of them is examined via simulation where the in-control parameters are estimated and error terms have a Student’s t distribution or gamma distribution. In order to capture the sampling variation among different practitioners\, average and standard deviation of the average run length (ARL) are used as performance measures. In conclusion\, it is seen that the estimation effect becomes more severe as the error term distribution deviates from normality to a greater extent. In addition\, although the average ARL values get closer to the desired values as the amount of Phase I data increases\, their standard deviations remain far away from the acceptable level indicating a high practitioner-to-practitioner variability.
URL:https://www.hsu-hh.de/statistik/event/effect-of-estimation-under-non-normality
LOCATION:Gebäude H1\, Raum 1503
CATEGORIES:Kolloquium
ORGANIZER;CN="F%C3%A4chergruppe Mathematik und Statistik":MAILTO:weissc@hsu-hh.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20190612T163000
DTEND;TZID=Europe/Berlin:20190612T180000
DTSTAMP:20190424T123625Z
CREATED:20190424T123524Z
LAST-MODIFIED:20190424T123625Z
UID:781-1560357000-1560362400@www.hsu-hh.de
SUMMARY:Amanda Fernández-Fontelo (HU Berlin)
DESCRIPTION:INAR-hidden Markov chains to deal with misreported count data \nSince McKenzie presented the classical non-negative integer-valued autoregressive (INAR)\nmodel\, the analysis of count time series has been rapidly growing in the past years\, and many\nauthors have been actively contributing to its improvement. However\, many issues remain to\nbe addressed in this field.\nIn the present work\, the authors introduce new models of count time series that accommodate\npotential misreporting in data. Misreporting in count data is a widespread concern that is\nresponsible for reporting inaccurate levels of data. This phenomenon can appear in terms of\nunder-reporting (less than the actual amount of data) or over-reporting (more than the actual\namount of data). However\, the believability of such data significantly decreases when both\nphenomena are present.
URL:https://www.hsu-hh.de/statistik/event/inar-hidden-markov-chains-to-deal-with-misreported-count-data
LOCATION:Gebäude H1\, Raum 1503
END:VEVENT
END:VCALENDAR