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Nr. LV-Typ(en) LV-Titel
5845 PI Advanced Topics in Dependence Modeling Präsenz-Modus
Anmeldung über LPIS
vom 19.02.2024 15:00 bis 01.03.2024 23:59
Abmeldung über LPIS
vom 19.02.2024 15:00 bis 05.04.2024 23:59

LV-Leiter/in Univ.Prof. Dr. Johana Genest Neslehova
Planpunkte Doktorat/PhD Vertiefung in den Forschungsmethoden der Sozial- und Wirtschaftswissenschaften
Forschungsmethoden
Methodologie und Theorie
Vertiefung in den Forschungsmethoden der Sozial- und Wirtschaftswissenschaften
Semesterstunden 2
Unterrichtssprache Englisch

Termine
Mo, 08.04.2024 10:00-13:00 Uhr TC.5.28 (Lageplan)
Di, 09.04.2024 10:00-13:00 Uhr D2.0.031 Workstation-Raum (Lageplan)
Mi, 10.04.2024 10:00-13:00 Uhr D2.0.031 Workstation-Raum (Lageplan)
Do, 11.04.2024 10:00-13:00 Uhr D2.0.031 Workstation-Raum (Lageplan)
Fr, 12.04.2024 10:00-13:00 Uhr D2.0.031 Workstation-Raum (Lageplan)
Do, 20.06.2024 10:00-13:00 Uhr Online-Einheit
Fr, 21.06.2024 10:00-13:00 Uhr Online-Einheit
Termindownload (ical) | Termine abonnieren

Weitere Informationen https://learn.wu.ac.at/vvz/24s/5845

Kontakt:
johanna.neslehova@mcgill.ca
Inhalte der LV:
This course will cover advanced topics in dependence modeling with copulas. After a brief review of copulas and copula models and statistical inference for such models in the bivariate case, the course will focus on copula modeling in higher dimensions, notably vine copula constructions, hierarchical models and factor copulas. We will also explore copula models for time series and the intricacies of copula modeling of discrete data and more generally of data with ties.
 
Assessment: 
 
The course assessment will be based on project work and on an oral presentation of the project.
 
Suggested reading:
 
  • An Introduction to Copulas by R. Nelsen, Springer 2007 (general reference for review of copulas and copula models)
  • Elements of Copula Modeling with R by M. Hofert, I. Kojadinovic, M. Mächler and J. Yan, Springer 2018 (general reference for review of inference for copula models)
  • Dependence Modeling with Copulas by H. Joe, CRC Press, Boca Raton, FL 2015
  • Analyzing Dependent Data with Vine Copulas by C. Czado, Springer 2019
  • Vine Copula Based Modeling by C. Czado and T. Nagler, Annu. Rev. Stat. Appl. 2022. 9:453–77

 

 

 
Lernergebnisse (Learning Outcomes):

Students will acquire a good understanding of theoretical and practical aspects of modeling dependent multivariate data with copulas. Moreover, they will be able to analyze data and use these models with R.
Regelung zur Anwesenheit:

at least 80% of the units
Lehr-/Lerndesign:

Classroom teaching; project and group work
Leistung(en) für eine Beurteilung:

The course assessment will be based on project work and on an oral presentation of the project.
Zuletzt bearbeitet: 17.01.2024 11:32
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