Syllabus

Title
6136 Advanced Topics in Statistics
Instructors
Univ.Prof. Dr. Johana Genest Neslehova
Contact details
johanna.neslehova@mcgill.ca
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
02/20/23 to 03/03/23
Registration via LPIS
Notes to the course
Subject(s) Doctoral/PhD Programs
Advanced Course Research Methods of Social and Economic Sciences
Research Methods
Methodology and Theory
Dates
Day Date Time Room
Wednesday 04/26/23 09:00 AM - 12:00 PM EA.5.040
Thursday 04/27/23 01:00 PM - 04:00 PM D4.0.019
Wednesday 05/03/23 09:00 AM - 12:00 PM D4.0.019
Thursday 05/04/23 09:00 AM - 12:00 PM D5.1.002
Thursday 06/22/23 12:30 PM - 03:30 PM D4.0.039
Friday 06/23/23 01:00 PM - 04:00 PM D4.0.127

On this page:

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Contents
This course will cover the modeling and statistical analysis extreme events in more complex settings than in the case of independent univariate variables. Topics include: (1) modeling extremes of dependent sequences and time series; (2) statistical models and methods for multivariate extremes, extreme-value copulas and point process models; (3) max-stable processes and spatial extremes.  
 
Assessment: 
 
The course assessment will be based on project work and on an oral presentation of the project.
 
Suggested reading: 

·      Modeling Extremal Events by P. Embrechts, C. Klüppelberg and T. Mikosch, Springer 1997 (Chapter 4)

·      An Introduction to Statistical Modeling of Extreme Values by S. Coles, Springer 2001 (Chapters 5 and 8)

·      Extremes and related properties of random sequences and processes by R. Leadbetter, G. Lindgren and H. Rootzén, Springer 1983

·      Statistics of Extremes by A.C. Davison and R. Huser, Annu. Rev. Stat. Appl. 2015. 2:203–35

·     Copula Modeling for Extremes by C. Genest and J.G. Nešlehová, Encyclopedia of Environmetrics (El-Shaarawi, Abdel H. and Piegorsch, Walter W. Eds.), Wiley 2012

 

 

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Learning outcomes

Students will acquire a good understanding of theoretical and practical aspects of  univariate EVT. Moreover, they will be able to analyze data with EVT.

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Attendance requirements

at least 80% of the units

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Teaching/learning method(s)

Classroom teaching; project and group work

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Assessment

The course assessment will be based on project work and on an oral presentation of the project.

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Readings

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Last edited: 2023-04-25



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