Research Group "Stochastic Algorithms and Nonparametric Statistics"

Research Seminar "Mathematical Statistics" SS 2022

  • Place: The seminar will be hybrid and realized via Zoom. Please note that the so-called ``3G rule" applies at Weierstrass Institute. Our lecture room ESH has according to hygiene recommendations only a capacity of 16 people. If you intend to participate you must register for our mailinglist with Andrea Fiebig ( Prior to each talk a doodle will be created where it is mandatory to sign in for attendance in person. Therefore, it is mandatory for those who want to participate in person to register (put your name in the list) using the doodle link sent by e-mail before the lecture. Please follow the streamed talk at , if 16 guests have already registered.
  • Time: Wednesdays, 10.00 a.m. - 12.30 p.m.
20.04.2022 Prof. Dr. Wolfgang Karl Härdle (BRC Blockchain Research Center)
代 DAI the Digital Art Index (hybrid talk)
The 代 DAI Digital Art Index has been developed to reflect the increasing activities on the the Digital Art market. Based on the most liquid exchanges, NFT data and prices are collected in cooperation with, NYC . The NFT art market has risen sharply recently and is competing with traditional arts market. The observed transactions are analysed and an index is developed on a hedonic regression framework. We present an introduction into NFTs, explain their construction and "huberize" the hedonic regression context.
27.04.2022 Dr. Nazgul Zakiyeva (Zuse-Institut Berlin/National University of Singapore)
Modeling and forecasting the dynamics of the natural gas transmission network in Germany with the demand and supply balance constraint (hybrid talk)
We develop a novel large-scale Network Autoregressive model with balance Constraint (NAC) to predict hour-ahead gas flows in the gas transmission network, where the total in- and out-flows of the network are balanced over time. By integrating recent advances in optimization and statistical modeling, the NAC model can provide an accurate hour-ahead forecast of the gas flow at all of the distribution points in the network. By detecting the influential nodes of the dynamic network, taking into account that demand and supply have to be balanced, the forecast can be used to compute an optimized schdule and resource allocation. We demonstrate an application of our model in forecasting hour-ahead gas in- and out-flows at 128 nodes in the German high-pressure natural gas transmission network over a time frame of 22 months. Link to the paper:
04.05.2022 Dr. Carlos Amendola (MPI Leipzig and TU Berlin)
Likelihood geometry of correlation models (hybrid talk)
Correlation matrices are standardized covariance matrices. They form an affine space of symmetric matrices defined by setting the diagonal entries to one. We study the geometry of maximum likelihood estimation for this model and linear submodels that encode additional symmetries. We also consider the problem of minimizing two closely related functions of the covariance matrix: the Stein's loss and the symmetrized Stein's loss. Unlike the Gaussian log-likelihood, these two functions are convex and hence admit a unique positive definite optimum. This is joint work with Piotr Zwiernik (University of Toronto).
11.05.2022 Prof. Dr. Vladimir Spokoiny (WIAS & HU Berlin)
Laplace approximation in high dimension with applications to statistical inference (hybrid talk)
This note revisits the classical results on Laplace approximation in a modern non-asymptotic and dimension free form. Such an extension is motivated by the uncertainty quantification for high dimensional statistical models. The established results provide an explicit non-asymptotic bounds on the quality of a Gaussian approximation of the posterior distribution in total variation distance in terms of the so called empheffective dimension p_G defined as interplay between information contained in the data and in the prior distribution. In the contrary to prominent Bernstein-von-Mises results, the impact of the prior is not negligible and it allows to keep the effective dimension small or moderate even if the true parameter dimension is huge or infinite. We also address the important issue of using a Laplace approximation with posterior mean in place of Maximum Aposteriori Probability (MAP).
18.05.2022 Dr. Zdeněk Hlávka (Charles University)
Testing dependencies in functional time series (hybrid talk)
We discuss tests of serial independence for a sequence of functional observations and tests of independence between two (or more) time series of functional observations. The tests are based on characteristic functions which are appropriately estimated from functional observations. The limit distribution of the new test statistic is obtained under the null hypothesis, while under alternatives it is shown that the test statistics almost surely diverge as the sample size increases. In a Monte Carlo study, we investigate appropriate resampling methods and we investigate the tests? performance in finite samples. Finally, an application illustrates the use of the method with real data from financial markets, including also cumulative intraday returns of Bitcoin and Ethereum.
25.05.2022 N. N.

01.06.2022 N. N.

08.06.2022 Mari Myllymäki (Natural Resources Institute Finland)
15.06.2022 Alexandra Carpentier (Universität Potsdam)
22.06.2022 Stanislav Minsker (University of Southern California)
29.06.2022 Anuj Srivastava (Florida State University)
06.07.2022 Vincent Rivoirard (Paris, Orsay)
MO 39 in room 406/405 tba
13.07.2022 N. N.
HVP 11a in room 3.13
20.07.2022 N. N.

last reviewed: May 23, 2022 by Christine Schneider