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
Advanced Course Research Methods of Social and Economic Sciences
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

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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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