University of Athens - Seminar lecture 16/12/2014: Change Point Inference in Dynamic Erdos-Renyi Random Graphs
Fri 12 Dec 2014 - 15:58
Σεμινάριο Στατιστικής και Επιχειρησιακής Έρευνας.
Ομιλητής: Καθηγητής κ. Γεώργιος Μιχαηλίδης Τμήμα Στατιστικής, University
of Michigan, ΗΠΑ.
Ημερομηνία-ώρα: Τρίτη 16 Δεκεμβρίου 2014 και ώρα 13:00.
Τόπος: Αίθουσα Α32 Τμήματος Μαθηματικών Πανεπιστημίου Αθηνών.
Τίτλος: Change Point Inference in Dynamic Erdos-Renyi Random Graphs
Περίληψη: We investigate a model of an Erdos-Renyi graph, where the edges can be in a set of finite states (e.g.present/absent). The states of each edge evolve as a Markov chain independently of the other edges, and whose parameters exhibit a change-point behavior in time. We derive the maximum likelihood estimator for the change-point and characterize its distribution. Depending on a measure of the signal-to-noise ratio present in the data, different limiting regimes emerge. Nevertheless, a unifying adaptive scheme can be used in practice that covers all cases. Finally, for appropriate choices of the parameters of the Markov kernels, the
limiting distribution of the change-point exhibits long-range dependence. The model is illustrated on synthetic, as well as real data.
Ομιλητής: Καθηγητής κ. Γεώργιος Μιχαηλίδης Τμήμα Στατιστικής, University
of Michigan, ΗΠΑ.
Ημερομηνία-ώρα: Τρίτη 16 Δεκεμβρίου 2014 και ώρα 13:00.
Τόπος: Αίθουσα Α32 Τμήματος Μαθηματικών Πανεπιστημίου Αθηνών.
Τίτλος: Change Point Inference in Dynamic Erdos-Renyi Random Graphs
Περίληψη: We investigate a model of an Erdos-Renyi graph, where the edges can be in a set of finite states (e.g.present/absent). The states of each edge evolve as a Markov chain independently of the other edges, and whose parameters exhibit a change-point behavior in time. We derive the maximum likelihood estimator for the change-point and characterize its distribution. Depending on a measure of the signal-to-noise ratio present in the data, different limiting regimes emerge. Nevertheless, a unifying adaptive scheme can be used in practice that covers all cases. Finally, for appropriate choices of the parameters of the Markov kernels, the
limiting distribution of the change-point exhibits long-range dependence. The model is illustrated on synthetic, as well as real data.
- PhD positions - Department of Mathematics of the University of Utrecht, Topic: random graphs and percolation theory
- UNIVERSITY OF PIRAEUS - SEMINAR LECTURE 27/3/2015: A version of the Fundamental Theorem of Asset Pricing
- UNIVERSITY OF PIRAEUS - SEMINAR LECTURE 24/11: The linear stochastic order and directed inference for multivariate ordered distributions
- UNIVERSITY OF PIRAEUS - SEMINAR LECTURES 29/4/2015
- AUEB SEMINARS MAY-JUNE 2014 (ΕΝΗΜΕΡΩΜΕΝΟ 21/5/2014)
Permissions in this forum:
You cannot reply to topics in this forum