@phdthesis{Michel2006, author = {Michel, Ren{\´e}}, title = {Simulation and Estimation in Multivariate Generalized Pareto Models}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-18489}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2006}, abstract = {The investigation of multivariate generalized Pareto distributions (GPDs) in the framework of extreme value theory has begun only lately. Recent results show that they can, as in the univariate case, be used in Peaks over Threshold approaches. In this manuscript we investigate the definition of GPDs from Section 5.1 of Falk et al. (2004), which does not differ in the area of interest from those of other authors. We first show some theoretical properties and introduce important examples of GPDs. For the further investigation of these distributions simulation methods are an important part. We describe several methods of simulating GPDs, beginning with an efficient method for the logistic GPD. This algorithm is based on the Shi transformation, which was introduced by Shi (1995) and was used in Stephenson (2003) for the simulation of multivariate extreme value distributions of logistic type. We also present nonparametric and parametric estimation methods in GPD models. We estimate the angular density nonparametrically in arbitrary dimension, where the bivariate case turns out to be a special case. The asymptotic normality of the corresponding estimators is shown. Also in the parametric estimations, which are mainly based on maximum likelihood methods, the asymptotic normality of the estimators is shown under certain regularity conditions. Finally the methods are applied to a real hydrological data set containing water discharges of the rivers Altm{\"u}hl and Danube in southern Bavaria.}, subject = {Pareto-Verteilung}, language = {en} } @book{FalkMarohnMicheletal.2011, author = {Falk, Michael and Marohn, Frank and Michel, Ren{\´e} and Hofmann, Daniel and Macke, Maria and Tewes, Bernward and Dinges, Peter and Spachmann, Christoph and Englert, Stefan}, 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-56489}, publisher = {Universit{\"a}t W{\"u}rzburg}, year = {2011}, 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. 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 three chapters can be dealt within the first semester. They provide the principal components of the analysis of a time series in the time domain. Chapters 4, 5 and 6 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. Chapter 7 (case study) deals with a practical case and demonstrates the presented methods. It is possible to use this chapter independent in a seminar or practical training course, if the concepts of time series analysis are already well understood. This book is consecutively subdivided in a statistical part and an SAS-specific part. For better clearness the SAS-specific parts are highlighted. This book is an open source project under the GNU Free Documentation License.}, subject = {Zeitreihenanalyse}, language = {en} } @book{FalkMarohnMicheletal.2005, 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 , Universit{\"a}t Eichst{\"a}tt/Rechenzentrum}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-12593}, publisher = {Universit{\"a}t W{\"u}rzburg}, year = {2005}, 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} } @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} } @book{FalkMarohnMicheletal.2012, author = {Falk, Michael and Marohn, Frank and Michel, Ren{\´e} and Hofmann, Daniel and Macke, Maria and Spachmann, Christoph and Englert, Stefan}, title = {A First Course on Time Series Analysis : Examples with SAS [Version 2012.August.01]}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-72617}, publisher = {Universit{\"a}t W{\"u}rzburg}, year = {2012}, 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. 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 three chapters can be dealt within the first semester. They provide the principal components of the analysis of a time series in the time domain. Chapters 4, 5 and 6 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. Chapter 7 (case study) deals with a practical case and demonstrates the presented methods. It is possible to use this chapter independent in a seminar or practical training course, if the concepts of time series analysis are already well understood. This book is consecutively subdivided in a statistical part and an SAS-specific part. For better clearness the SAS-specific parts are highlighted. This book is an open source project under the GNU Free Documentation License.}, subject = {Zeitreihenanalyse}, language = {en} } @article{BousquetAntoBachertetal.2021, author = {Bousquet, Jean and Anto, Josep M. and Bachert, Claus and Haahtela, Tari and Zuberbier, Torsten and Czarlewski, Wienczyslawa and Bedbrook, Anna and Bosnic-Anticevich, Sinthia and Walter Canonica, G. and Cardona, Victoria and Costa, Elisio and Cruz, Alvaro A. and Erhola, Marina and Fokkens, Wytske J. and Fonseca, Joao A. and Illario, Maddalena and Ivancevich, Juan-Carlos and Jutel, Marek and Klimek, Ludger and Kuna, Piotr and Kvedariene, Violeta and Le, LTT and Larenas-Linnemann, D{\´e}sir{\´e}e E. and Laune, Daniel and Louren{\c{c}}o, Olga M. and Mel{\´e}n, Erik and Mullol, Joaquim and Niedoszytko, Marek and Odemyr, Mika{\"e}la and Okamoto, Yoshitaka and Papadopoulos, Nikos G. and Patella, Vincenzo and Pfaar, Oliver and Pham-Thi, Nh{\^a}n and Rolland, Christine and Samolinski, Boleslaw and Sheikh, Aziz and Sofiev, Mikhail and Suppli Ulrik, Charlotte and Todo-Bom, Ana and Tomazic, Peter-Valentin and Toppila-Salmi, Sanna and Tsiligianni, Ioanna and Valiulis, Arunas and Valovirta, Erkka and Ventura, Maria-Teresa and Walker, Samantha and Williams, Sian and Yorgancioglu, Arzu and Agache, Ioana and Akdis, Cezmi A. and Almeida, Rute and Ansotegui, Ignacio J. and Annesi-Maesano, Isabella and Arnavielhe, Sylvie and Basaga{\~n}a, Xavier and D. Bateman, Eric and B{\´e}dard, Annabelle and Bedolla-Barajas, Martin and Becker, Sven and Bennoor, Kazi S. and Benveniste, Samuel and Bergmann, Karl C. and Bewick, Michael and Bialek, Slawomir and E. Billo, Nils and Bindslev-Jensen, Carsten and Bjermer, Leif and Blain, Hubert and Bonini, Matteo and Bonniaud, Philippe and Bosse, Isabelle and Bouchard, Jacques and Boulet, Louis-Philippe and Bourret, Rodolphe and Boussery, Koen and Braido, Fluvio and Briedis, Vitalis and Briggs, Andrew and Brightling, Christopher E. and Brozek, Jan and Brusselle, Guy and Brussino, Luisa and Buhl, Roland and Buonaiuto, Roland and Calderon, Moises A. and Camargos, Paulo and Camuzat, Thierry and Caraballo, Luis and Carriazo, Ana-Maria and Carr, Warner and Cartier, Christine and Casale, Thomas and Cecchi, Lorenzo and Cepeda Sarabia, Alfonso M. and H. Chavannes, Niels and Chkhartishvili, Ekaterine and Chu, Derek K. and Cingi, Cemal and Correia de Sousa, Jaime and Costa, David J. and Courbis, Anne-Lise and Custovic, Adnan and Cvetkosvki, Biljana and D'Amato, Gennaro and da Silva, Jane and Dantas, Carina and Dokic, Dejan and Dauvilliers, Yves and De Feo, Giulia and De Vries, Govert and Devillier, Philippe and Di Capua, Stefania and Dray, Gerard and Dubakiene, Ruta and Durham, Stephen R. and Dykewicz, Mark and Ebisawa, Motohiro and Gaga, Mina and El-Gamal, Yehia and Heffler, Enrico and Emuzyte, Regina and Farrell, John and Fauquert, Jean-Luc and Fiocchi, Alessandro and Fink-Wagner, Antje and Fontaine, Jean-Fran{\c{c}}ois and Fuentes Perez, Jos{\´e} M. and Gemicioğlu, Bilun and Gamkrelidze, Amiran and Garcia-Aymerich, Judith and Gevaert, Philippe and Gomez, Ren{\´e} Maximiliano and Gonz{\´a}lez Diaz, Sandra and Gotua, Maia and Guldemond, Nick A. and Guzm{\´a}n, Maria-Antonieta and Hajjam, Jawad and Huerta Villalobos, Yunuen R. and Humbert, Marc and Iaccarino, Guido and Ierodiakonou, Despo and Iinuma, Tomohisa and Jassem, Ewa and Joos, Guy and Jung, Ki-Suck and Kaidashev, Igor and Kalayci, Omer and Kardas, Przemyslaw and Keil, Thomas and Khaitov, Musa and Khaltaev, Nikolai and Kleine-Tebbe, Jorg and Kouznetsov, Rostislav and Kowalski, Marek L. and Kritikos, Vicky and Kull, Inger and La Grutta, Stefania and Leonardini, Lisa and Ljungberg, Henrik and Lieberman, Philip and Lipworth, Brian and Lodrup Carlsen, Karin C. and Lopes-Pereira, Catarina and Loureiro, Claudia C. and Louis, Renaud and Mair, Alpana and Mahboub, Bassam and Makris, Micha{\"e}l and Malva, Joao and Manning, Patrick and Marshall, Gailen D. and Masjedi, Mohamed R. and Maspero, Jorge F. and Carreiro-Martins, Pedro and Makela, Mika and Mathieu-Dupas, Eve and Maurer, Marcus and De Manuel Keenoy, Esteban and Melo-Gomes, Elisabete and Meltzer, Eli O. and Menditto, Enrica and Mercier, Jacques and Micheli, Yann and Miculinic, Neven and Mihaltan, Florin and Milenkovic, Branislava and Mitsias, Dimitirios I. and Moda, Giuliana and Mogica-Martinez, Maria-Dolores and Mohammad, Yousser and Montefort, Steve and Monti, Ricardo and Morais-Almeida, Mario and M{\"o}sges, Ralph and M{\"u}nter, Lars and Muraro, Antonella and Murray, Ruth and Naclerio, Robert and Napoli, Luigi and Namazova-Baranova, Leyla and Neffen, Hugo and Nekam, Kristoff and Neou, Angelo and Nordlund, Bj{\"o}rn and Novellino, Ettore and Nyembue, Dieudonn{\´e} and O'Hehir, Robyn and Ohta, Ken and Okubo, Kimi and Onorato, Gabrielle L. and Orlando, Valentina and Ouedraogo, Solange and Palamarchuk, Julia and Pali-Sch{\"o}ll, Isabella and Panzner, Peter and Park, Hae-Sim and Passalacqua, Gianni and P{\´e}pin, Jean-Louis and Paulino, Ema and Pawankar, Ruby and Phillips, Jim and Picard, Robert and Pinnock, Hilary and Plavec, Davor and Popov, Todor A. and Portejoie, Fabienne and Price, David and Prokopakis, Emmanuel P. and Psarros, Fotis and Pugin, Benoit and Puggioni, Francesca and Quinones-Delgado, Pablo and Raciborski, Filip and Rajabian-S{\"o}derlund, Rojin and Regateiro, Frederico S. and Reitsma, Sietze and Rivero-Yeverino, Daniela and Roberts, Graham and Roche, Nicolas and Rodriguez-Zagal, Erendira and Rolland, Christine and Roller-Wirnsberger, Regina E. and Rosario, Nelson and Romano, Antonino and Rottem, Menachem and Ryan, Dermot and Salim{\"a}ki, Johanna and Sanchez-Borges, Mario M. and Sastre, Joaquin and Scadding, Glenis K. and Scheire, Sophie and Schmid-Grendelmeier, Peter and Sch{\"u}nemann, Holger J. and Sarquis Serpa, Faradiba and Shamji, Mohamed and Sisul, Juan-Carlos and Sofiev, Mikhail and Sol{\´e}, Dirceu and Somekh, David and Sooronbaev, Talant and Sova, Milan and Spertini, Fran{\c{c}}ois and Spranger, Otto and Stellato, Cristiana and Stelmach, Rafael and Thibaudon, Michel and To, Teresa and Toumi, Mondher and Usmani, Omar and Valero, Antonio A. and Valenta, Rudolph and Valentin-Rostan, Marylin and Pereira, Marilyn Urrutia and van der Kleij, Rianne and Van Eerd, Michiel and Vandenplas, Olivier and Vasankari, Tuula and Vaz Carneiro, Antonio and Vezzani, Giorgio and Viart, Fr{\´e}d{\´e}ric and Viegi, Giovanni and Wallace, Dana and Wagenmann, Martin and Wang, De Yun and Waserman, Susan and Wickman, Magnus and Williams, Dennis M. and Wong, Gary and Wroczynski, Piotr and Yiallouros, Panayiotis K. and Yusuf, Osman M. and Zar, Heather J. and Zeng, St{\´e}phane and Zernotti, Mario E. and Zhang, Luo and Shan Zhong, Nan and Zidarn, Mihaela}, title = {ARIA digital anamorphosis: Digital transformation of health and care in airway diseases from research to practice}, series = {Allergy}, volume = {76}, journal = {Allergy}, number = {1}, doi = {10.1111/all.14422}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-228339}, pages = {168 -- 190}, year = {2021}, abstract = {Digital anamorphosis is used to define a distorted image of health and care that may be viewed correctly using digital tools and strategies. MASK digital anamorphosis represents the process used by MASK to develop the digital transformation of health and care in rhinitis. It strengthens the ARIA change management strategy in the prevention and management of airway disease. The MASK strategy is based on validated digital tools. Using the MASK digital tool and the CARAT online enhanced clinical framework, solutions for practical steps of digital enhancement of care are proposed.}, language = {en} }