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Statistics Seminar| A semi-Parametric Way of Multivariate Modelling in the Framework of Pair Copula Construction

Start Date: February 13, 2018
End Date: February 13, 2018

By Prof. Mimi Zhang (Trinity College Dublin)
 
Pair copula construction (PCC) is a powerful tool for multivariate modeling, which decomposes a multivariate distribution into a hierarchy of bivariate copulas and conditional bivariate copulas. The main challenge of PCC is modeling conditional bivariate copulas, each of which is a family of distributions: one for every valid value of the conditioning variable. This problem, though recognized as crucial in the field of multivariate modelling, remains widely unexplored due to its inherent complication. Rather than resorting to traditional parametric or non-parametric methods, I will discuss the approach of approximating a conditional copula, to any required degree of approximation, by utilizing a family of basis functions. We fully incorporate the impact of the conditioning variables on the functional form of a conditional copula by employing local learning methods.
 
Biography: Mimi Zhang joined TCD as an assistant professor in October 2017. Mimi holds a B.Sc. in statistics from University of Science and Technology of China, and a Ph.D. in engineering management from City University of Hong Kong. Before joining TCD, she was a research associate at University of Strathclyde and Imperial College London. Her main research areas include Markov Decision Process, Stochastic Modelling, Multivariate Modelling and Data Mining.
 

More Information:

For more info contact: Distinguished Prof. Marc Genton: email: marc.genton@kaust.edu.sa
 
Date: Tuesday 13th Feb 2018
Time:04:00 PM - 05:00 PM
Location: Building 1, Level 4, room# 4102
Refreshments: Light refreshment will be served around 3:45 PM