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BEGIN:VEVENT
DTSTAMP:20260406T142202Z
DTSTART;VALUE=DATE-TIME:20230307T170000
DTEND;VALUE=DATE-TIME:20230307T180000
SUMMARY:YRM W9 - Fan Wang on Online change point localization in multi-la
 yer random dot product graphs
TZID:Europe/London
UID:20230307-8a1785d785a156320185a67454c42634@warwick.ac.uk
CREATED:20230306T114738Z
DESCRIPTION:Title: Online change point localization in multi-layer random
  dot product graphs. Abstract: We study the online change point localiza
 tion problem in dynamic multilayer random dot product graphs (D-MRDPGs).
  To be specific\, at every time point\, we extend the nonparametric rand
 om dot product graph (RDPG) to the directed multilayer graph with the co
 mmon node sets and the underlying distributions change when a change poi
 nt occurs. The goal is to detect the change point as quickly as possible
  if it exists\, subject to a constraint on the probability of false alar
 ms. The change in our framework is in fact\, a multivariate nonparametri
 c distributional change. Instead of defining the jump size using a densi
 ty\, we use the expectation of the kernel density estimator (KDE). Our g
 eneric model setting allows the model parameters\, including the number 
 of nodes\, the dimension of latent position\, and the magnitude of the c
 hange\, to vary as functions of the location of the change point. We pro
 pose a novel change point detection algorithm and proved an upper bound 
 on the detection delay under a signal-to-noise ratio condition. Addition
 ally\, for a single multilayer random dot product graph (MRDPG)\, we stu
 dy an estimation method based on the adjacency tensor and show it achiev
 es the optimal statistical performance in estimation error. Finally\, ex
 tensive numerical results including simulation studies and real data stu
 dies are provided to support our theoretical results.
LOCATION:
CATEGORIES:
LAST-MODIFIED:20230306T114738Z
ORGANIZER;CN=Claudia Viaro:
END:VEVENT
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