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UID:20251124T201153EST-2860D24jzl@132.216.98.100
DTSTAMP:20251125T011153Z
DESCRIPTION:Title: About the multivariate fractional Brownian motion.\n\nAb
 stract: Since the pioneering work by Mandelbrot and Van Ness in 1968\, the
  fractional Brownian motion (fBm) became a classical stochastic process fo
 r modelling one-dimesional self-similar or long-memory processes. In parti
 c- ular\, we have recently applied this model to characterize the regulari
 ty and dependence of fMRI signals acquired in the brain of resting-state p
 atients. This analysis was conducted independently on each region of inter
 est of the brain. Despite the first analysis showed interesting results\, 
 the model needed to be improved in order to take into account the possible
  connectivity of regions of interest. In this talk\, we present an extensi
 on of the fBm to the multivariate case that may be well-suited to such dat
 a: the multivariate fractional Brownian motion (mfBm) characterized in par
 ticular by p Hurst exponents. After recalling some facts about the fBm\, I
  will state some theoretical properties of the mfBm: (cross)-correlation\,
  spectral density matrix\, wavelet analysis\, existence conditions. Then\,
  I will detail how we can exactly and quickly generate sample paths of the
  mfBm. Finally\, we will focus on the statistical inference and mainly on 
 the joint estimation of the fractal exponents (H1\, ...\, Hp) using a disc
 rete variations techniques.\n
DTSTART:20190221T203000Z
DTEND:20190221T213000Z
LOCATION:Room PK-5115 \, CA\, UQAM\, Seminar STATQAM
SUMMARY:Jean-François Coeurjolly\, UQAM
URL:/mathstat/channels/event/jean-francois-coeurjolly-
 uqam-294776
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