Forschungsseminar

Forschungsseminar

Wir laden alle Interessierten herzlich ein, unser Forschungsseminar zu besuchen.

Univ.-Prof. Mag.Dr. Andreas Futschik, Univ.-Prof. Mag.Dr. Werner G. Müller

Institut für Angewandte Statistik

Forschungsseminar

Zeit

Donnerstags von 15:30 - 17:00

Ort

Science Park 2, Zwischengeschoß, Z74

Sommersemester 2021

  1. Online-Vortrag - 25. März - Dr. Matt Sutton, QUT, Australia: Reversible Jump PDMP Samplers for Variable Selection

    Zoom-Link zu den Online-Vorträgen

    Abstract:

    A new class of Markov chain Monte Carlo (MCMC) algorithms, based on simulating piecewise deterministic Markov processes (PDMPs), have recently shown great promise: they are non-reversible, can mix better than standard MCMC algorithms, and can use subsampling ideas to speed up computation in big data scenarios. However, current PDMP samplers can only sample from posterior densities that are differentiable almost everywhere, which precludes their use for model choice. Motivated by variable selection problems, we show how to develop reversible jump PDMP samplers that can jointly explore the discrete space of models and the continuous space of parameters. Our framework is general: it takes any existing PDMP sampler and adds two types of trans-dimensional moves that allow for the addition or removal of a variable from the model. We show how the rates of these trans-dimensional moves can be calculated so that the sampler has the correct invariant distribution. Simulations show that the new samplers can mix better than standard MCMC algorithms. Our empirical results show they are also more efficient than gradient-based samplers that avoid model choice through use of continuous spike-and-slab priors which replace a point mass at zero for each parameter with a density concentrated around zero.

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Vergangene Vorträge

Wintersemester 2020/21

  1. Online-Vortrag - 28. Jänner - Ulrike Schneider, TU Wien:

    Zoom-Link zu den Online-Vorträgen

  2. Online-Vortrag - 21. Jänner - Lisa Ehrlinger & Florian Sobieczky, Software Competence Center Hagenberg: A Rendez-Vous in Data Science: Machine Learning meets Statistics

    Zoom-Link zu den Online-Vorträgen

    Abstract:

    The talk covers several typical challenges from “Data Science” arising in research
    projects at the Software Competence Center Hagenberg (SCCH). Classical statistics
    as well as modern complex machine learning methods, such as neural networks, are
    applied to real-world use cases from industry.
    In the first part, a short presentation of SCCH as an institution for applied research
    is given, which is particularly interesting for students with an interest in a master or
    PhD thesis on practical problems.
    The second part is a summary of various projects involving real-world data with a
    focus on recurring statistical problems from manufacturing scenarios. In particular,
    methods related to anomaly detection, diagnosis and prediction using machine
    learning methods are discussed with some care given to the black-box stigma of typical
    modern machine learning methods. The presentation is intended to identify classical
    methods and open research questions from statistics relevant for approaches taken by
    SCCH’s strategy on predictive maintenance.
    *SCCH – Software Competence Center Hagenberg
    **FAW - Institute for Application-oriented Knowledge Processing der JKU

  3. Online-Vortrag - 19. November - Zsolt Lavicza & Martin Andre, Johannes Kepler University in Linz & Universität Innsbruck: Technology changing statistics education: Defining possibilities, opportunities and obligations.

    Folien des Vortrages

    Abstract:

    In our talk, we will online some educational research activities within the Linz School of Education related to technology developments and statistics education. Afterwards, we will discuss our work on introducing statistics concepts in schools and how statistics teaching can be connected to sustainable development with real data for students in schools. In particular, we will discuss that statistics is becoming crucial in our current data-driven society to explore numerous phenomena that are too complex to comprehend without exploring and visualising data. Citizens need to understand statistics about issues concerning essential parts of their lives such as the spread of a pandemic or climate change in order to responsibly participate in a prosperous development of our civilization. With our research projects we try to find out more about young students’ intuitive approaches to statistics when visually analysing data. We found that certain kinds of data visualisations are especially capable to provoke reasoning of statistical concepts such as ideas of centre, spread and covariation. Based on these intuitive visual approaches to statistics, another aspect of our design-based research projects is concerned with statistical modelling processes. We developed a learning trajectory where middle school students were engaged in analysing real-world data to explore sustainable development of various countries and to build a model for this phenomenon. Results show that students’ statistical investigative learning processes should feature active participation in constructing knowledge of formal statistical concepts; and students should adopt and fit their intuitive knowledge to formal concepts using methods of visual data analyses. We will outline some diverse opportunities to foster students’ intuitive understanding of statistics and sustainable development issues simultaneously.

    Zoom-Link zu den online-Vorträgen

  4. Online-Vortrag - 12. November - Irene Tubikanec, Johannes Kepler University in Linz: Approximate Bayesian computation for stochastic differential equations with an invariant distribution

    Folien des Vortrages

    Abstract:
    Approximate Bayesian computation (ABC) has become one of the major tools of likelihood-free statistical inference in complex mathematical models. Simultaneously, stochastic differential equations (SDEs) have developed to be an established tool for modelling time-dependent, real-world phenomena with underlying random effects. When applying ABC to stochastic models, two major difficulties arise. First, the derivation of effective summary statistics and proper distances is particularly challenging, since simulations from the stochastic process under the same parameter configuration result in different trajectories. Second, exact simulation schemes to generate trajectories from the stochastic model are rarely available, requiring the derivation of suitable numerical methods for the synthetic data generation. To obtain summaries that are less sensitive to the intrinsic stochasticity of the model, we propose to build up the statistical method (e.g. the choice of the summary statistics) on the underlying structural properties of the model. Here, we focus on the existence of an invariant measure and we map the data to their estimated invariant density and invariant spectral density. Then, to ensure that these model properties are kept in the synthetic data generation, we adopt measure-preserving numerical splitting schemes. The derived property-based and measure-preserving ABC method is illustrated on the broad class of partially observed Hamiltonian type SDEs, both with simulated data and with real electroencephalography data. The derived summaries are particularly robust to the model simulation, and this fact, combined with the proposed reliable numerical scheme, yields accurate ABC inference. In contrast, the inference returned using standard numerical methods (Euler–Maruyama discretization) fails. The proposed ingredients can be incorporated into any type of ABC algorithm and directly applied to all SDEs that are characterized by an invariant distribution and for which a measure-preserving numerical method can be derived.

    zoom-link zum online Vortrag

     

  5. Online-Vortrag - 5. November - Alex Kowarik, Statistics Austria: COVID-19 Prävalenzstudie - War die Stichprobe groß genug? 3.000 Marsmännchen, Ergebnisse und mehr

    Abstract:
    Im November wird bereits zum dritten Mal eine Stichprobenerhebung zur Bestimmung der COVID-19 Prävalenz durchgeführt. Der Vortrag soll die methodischen Aspekte Stichprobenziehung, Gewichtung und Fehlerrechnung dieser Erhebungen beleuchten.

    Folien des Vortrages


    Für Zugangsdaten zu diesem online-talk wenden Sie sich bitte an Milan Stehlik

     

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Sommersemester 2020

  1. Online-Vortrag - 18. Juni - Torsten Hothorn, Universität Zürich, Schweiz: Understanding and Applying Transformation Models

    [Abstract]

    Für Zugangsdaten zu diesem online-talk wenden Sie sich bitte an Markus Hainy

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Wintersemester 2019/20

  1. 23. Jänner 2020

    Peter Filzmoser, Technische Universität Wien

    Robust and sparse k-means clustering in high dimension

    [abstract]

  2. 05. Dezember 2019

    Hao Wang, Jilin University, Changchun

    Dependence structure between Chinese Shanghai and Shenzhen stock market based on copulas and cluster analysis

    [abstract]

     

  3. 28. November 2019

    Haipeng Li, CAS-MPG, Shanghai

    Supervised learning for analyzing large-scale genome-wide DNA polymorphism data

    [abstract]

     

  4. 07. November 2019

    Günter Pilz, Johannes Kepler Universität Linz

    Statistik ist ein Segen für die Menschheit

    [abstract]

    [Vortrag]

  5. 31. Oktober 2019

    Martin Wolfsegger, Takeda Pharmaceutical Company Ltd.

    Some likely useful thoughts on prescription drug-use-related software support­ing personalized dosing regimen

    Alexander Bauer, Takeda Pharmaceutical Company Ltd.

    Evaluation of drug combinations

    [abstract]

  6. 10. Oktober 2019

    Leonardo Grilli, University of Florence

    Multiple imputation and selection of predictors in multilevel models for analys­ing the relationship between student ratings and teacher beliefs and practices

    [abstract]

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Sommersemester 2019

  1. 23. Mai 2019
    Siegfried Hörmann, TU Graz: ANOVA for functional time series data: when there is dependence between groups

    [abstract]

  2. 9. Mai 2019
    Markus Hainy, Johannes Kepler Universität Linz: Optimal Bayesian design for models with intractable likelihoods via supervised learning
    methods

    [abstract]

     

  3. 11. April 2019
    Dominik Schrempf, Eötvös Loránd University in Budapest, Ungarn: Phylogenetic incongruences - opportunities to improve the reconstruction of a dated tree of life

    [abstract]

     

  4. 4. April 2019
    Antony Overstall, University of Southampton, UK: Bayesian design for physical models using computer experiments

    [abstract]

  5. 14. März 2019
    Florian Frommlet, Medizinische Universität Wien: Deep Bayesian Regression

    [abstract]

  6. 14. März 2019. Achtung, Beginn: 13:45
    Thomas Petzoldt, TU Dresden, Deutschland: Identification of distribution components from antibiotic resistance data - Opportunities and challenges

    [abstract]

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Wintersemester 2018/19

  1. 17. Jänner 2019
    Harry Haupt, Universität Passau, Deutschland: Modeling spatial components for complexly associated urban data

    [abstract]

  2. 22. November 2018 (Achtung, Mittwoch 15:30, S3 048)
    Hirohisa Kishino, University of Tokyo, Japan: Bridging molecular evolution and phenotypic evolution

    [abstract]

  3. 15. November 2018
    Helmut Küchenhoff, Ludwig-Maximilians-Universität München, The analysis of voter transitions in the Bavarian state election 2018 using data from different sources: a teaching research project conducted by three Bavarian universities

    [abstract]

    [Folien]

  4. 8. November 2018
    Efstathia Bura, TU Wien: Least Squares and ML Estimation Approaches of the Sufficient Reduction for Matrix Valued Predictors

    [abstract]

  5. 25. Oktober 2018
    Erindi Allaj: Volatility measurement in presence of high-frequency data

    [abstract]

  6. 11. Oktober 2018
    David Gabauer, JKU Linz, Austria: To Be or Not to Be’ a Member of an Optimum Currency Area?

    [abstract]

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Sommersemester 2018

  1. 24. Mai 2018
    Carsten Wiuf, University of Copenhagen, Dänemark: A simple method to aggregate p-values without a priori grouping. 

  2. 24. Mai 2018
    Pavlina Jordanova, University of Shumen, Bulgaria: On “multivariate” modifications of Cramer Lundberg risk model. 

  3. 26. April 2018
    Juan M. Rodríguez-Díaz, Universidad de Salamanca, Spanien: Design optimality in multiresponse models with double covariance structure. 

  4. 19. April 2018
    Robert Breitenecker, Johannes Kepler Universität Linz: Spatial Heterogeneity in Entrepreneurship Research: An application of Geographically Weighted Regression

  5. 15. März 2018
    Andreas Mayr, Friedrich-Alexander-University Erlangen-Nürnberg, Germany: An introduction to boosting distributional regression. 

  6. 28. Juni 2018
    Gangaram S. Ladde, University of South Florida, USA: Energy/Lyapunov Function Method and Stochastic Mathematical Finance

    [abstract]

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Wintersemester 2017/18

  1. 25. January 2018
    Thomas Kneib, Georg-August-Universität Göttingen: A Lego System for Building Structured Additive Distributional Regression Models with Tensor Product Interactions

  2. 7. December 2017
    Franz König, Medizinische Universität Wien: Optimal rejection regions for multi-arm clinical trials

    [abstract]

  3. 9. November 2017
    Henrique Teotonio, Institut de Biologie de l'École Normale Supérieure, Paris: Inferring natural selection and genetic drift in evolution experiments

     

  4. 19. October 2017
    Lenka Filová, Comenius University in Bratislava: Optimal Design of Experiments in R

  5. 12. October 2017
    Elisa Perrone, Massachusetts Institute of Technology, Cambridge, MA (USA): Discrete copulas for weather forecasting: theoretical and practical aspects

    [abstract]

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