@book{FalkMarohnMicheletal.2006,
  author    = {Falk, Michael and Marohn, Frank and Michel, Ren{\´e} and Hofmann, Daniel and Macke, Maria and Tewes, Bernward and Dinges, Peter},
  title     = {A First Course on Time Series Analysis : Examples with SAS},
  organization    = {Universit{\"a}t W{\"u}rzburg / Lehrstuhl f{\"u}r Statistik},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-16919},
  publisher      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2006},
  abstract  = {The analysis of real data by means of statistical methods with the aid of a software package common in industry and administration usually is not an integral part of mathematics studies, but it will certainly be part of a future professional work. The present book links up elements from time series analysis with a selection of statistical procedures used in general practice including the statistical software package SAS Statistical Analysis System). Consequently this book addresses students of statistics as well as students of other branches such as economics, demography and engineering, where lectures on statistics belong to their academic training. But it is also intended for the practician who, beyond the use of statistical tools, is interested in their mathematical background. Numerous problems illustrate the applicability of the presented statistical procedures, where SAS gives the solutions. The programs used are explicitly listed and explained. No previous experience is expected neither in SAS nor in a special computer system so that a short training period is guaranteed. This book is meant for a two semester course (lecture, seminar or practical training) where the first two chapters can be dealt with in the first semester. They provide the principal components of the analysis of a time series in the time domain. Chapters 3, 4 and 5 deal with its analysis in the frequency domain and can be worked through in the second term. In order to understand the mathematical background some terms are useful such as convergence in distribution, stochastic convergence, maximum likelihood estimator as well as a basic knowledge of the test theory, so that work on the book can start after an introductory lecture on stochastics. Each chapter includes exercises. An exhaustive treatment is recommended. This book is consecutively subdivided in a statistical part and an SAS-specific part. For better clearness the SAS-specific part, including the diagrams generated with SAS, always starts with a computer symbol, representing the beginning of a session at the computer, and ends with a printer symbol for the end of this session. This book is an open source project under the GNU Free Documentation License.},
  subject      = {Zeitreihenanalyse},
  language  = {en}
}
@article{FalkFuller2021,
  author    = {Falk, Michael and Fuller, Timo},
  title     = {New characterizations of multivariate Max-domain of attraction and D-Norms},
  series = {Extremes},
  volume    = {24},
  journal   = {Extremes},
  number    = {4},
  issn      = {1572-915X},
  doi       = {10.1007/s10687-021-00416-4},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-269071},
  pages     = {849-879},
  year      = {2021},
  abstract  = {In this paper we derive new results on multivariate extremes and D-norms. In particular we establish new characterizations of the multivariate max-domain of attraction property. The limit distribution of certain multivariate exceedances above high thresholds is derived, and the distribution of that generator of a D-norm on R\(^{d}\), whose components sum up to d, is obtained. Finally we introduce exchangeable D-norms and show that the set of exchangeable D-norms is a simplex.},
  language  = {en}
}
@article{HerrmannLotzKaragiannidisetal.2022,
  author    = {Herrmann, Johannes and Lotz, Christopher and Karagiannidis, Christian and Weber-Carstens, Steffen and Kluge, Stefan and Putensen, Christian and Wehrfritz, Andreas and Schmidt, Karsten and Ellerkmann, Richard K. and Oswald, Daniel and Lotz, G{\"o}sta and Zotzmann, Viviane and Moerer, Onnen and K{\"u}hn, Christian and Kochanek, Matthias and Muellenbach, Ralf and Gaertner, Matthias and Fichtner, Falk and Brettner, Florian and Findeisen, Michael and Heim, Markus and Lahmer, Tobias and Rosenow, Felix and Haake, Nils and Lepper, Philipp M. and Rosenberger, Peter and Braune, Stephan and Kohls, Mirjam and Heuschmann, Peter and Meybohm, Patrick},
  title     = {Key characteristics impacting survival of COVID-19 extracorporeal membrane oxygenation},
  series = {Critical Care},
  volume    = {26},
  journal   = {Critical Care},
  number    = {1},
  doi       = {10.1186/s13054-022-04053-6},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-299686},
  year      = {2022},
  abstract  = {Background Severe COVID-19 induced acute respiratory distress syndrome (ARDS) often requires extracorporeal membrane oxygenation (ECMO). Recent German health insurance data revealed low ICU survival rates. Patient characteristics and experience of the ECMO center may determine intensive care unit (ICU) survival. The current study aimed to identify factors affecting ICU survival of COVID-19 ECMO patients. Methods 673 COVID-19 ARDS ECMO patients treated in 26 centers between January 1st 2020 and March 22nd 2021 were included. Data on clinical characteristics, adjunct therapies, complications, and outcome were documented. Block wise logistic regression analysis was applied to identify variables associated with ICU-survival. Results Most patients were between 50 and 70 years of age. PaO\(_{2}\)/FiO\(_{2}\) ratio prior to ECMO was 72 mmHg (IQR: 58-99). ICU survival was 31.4\%. Survival was significantly lower during the 2nd wave of the COVID-19 pandemic. A subgroup of 284 (42\%) patients fulfilling modified EOLIA criteria had a higher survival (38\%) (p = 0.0014, OR 0.64 (CI 0.41-0.99)). Survival differed between low, intermediate, and high-volume centers with 20\%, 30\%, and 38\%, respectively (p = 0.0024). Treatment in high volume centers resulted in an odds ratio of 0.55 (CI 0.28-1.02) compared to low volume centers. Additional factors associated with survival were younger age, shorter time between intubation and ECMO initiation, BMI > 35 (compared to < 25), absence of renal replacement therapy or major bleeding/thromboembolic events. Conclusions Structural and patient-related factors, including age, comorbidities and ECMO case volume, determined the survival of COVID-19 ECMO. These factors combined with a more liberal ECMO indication during the 2nd wave may explain the reasonably overall low survival rate. Careful selection of patients and treatment in high volume ECMO centers was associated with higher odds of ICU survival.},
  language  = {en}
}