We have developed a new method to estimate multiple change-points that may exist in a sequence of
observations. The method consists of a specific empirical Bayesian information criterion (emBIC) to assess
the fitness and virtue of each candidate configuration of change-points, and also a specific Gibbs sampling
induced stochastic search algorithm to find the optimal change-points configuration. It is shown that emBIC
can significantly improve over BIC that is known to have tendency of over-detecting multiple change-points.
The use of the stochastic search induced by Gibbs sampling enables one to find the optimal change-points
configuration with high probability and without going through an exhaustive search that is mostly computationally
infeasible. Simulation studies and real data examples are presented to illustrate and assess the proposed method.
How to participate in this seminar:
1. Book your nearest ACE facility;
2. Notify the seminar convenor at La Trobe University (Andriy Olenko) to notify you will be participating.
No access to an ACE facility? Contact Maaike Wienk to arrange a temporary Visimeet licence for remote access (limited number of licences available – first come first serve)