Multiple change-point detection via VIF regression

Speaker: Dr. Yuehua (Amy) Wu, Professor, Department of
Mathematics and Statistics, York University

Abstract: In this talk, we present a procedure for detecting multiple
change-points in a mean-shift model, where the number of change-points
is allowed to increase with the sample size. A theoretic justification
for this method is also given. We first convert the change-point
problem into a variable selection problem by partitioning the data
sequence into several segments. Then, we apply a modified variance
inflation factor regression algorithm to each segment in sequential
order. When a segment that is suspected of containing a change-point
is found, we use a weighted cumulative sum to test if there is indeed
a change-point in this segment. The procedure is implemented in an
algorithm which, compared to two popular methods via simulation
studies, demonstrates satisfactory performance in terms of accuracy,
stability and computation time. Real data examples are also provided.