Far East Journal of Theoretical Statistics
Volume 19, Issue 2, Pages 231 - 244
(July 2006)
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MULTIPLE TEMPORAL CLUSTER DETECTION TEST USING EXPONENTIAL
INEQUALITIES
Christophe Demattei (France) and Nicolas Molinari (France)
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Abstract: The method of Molinari et al.
[Biometrics 57 (2001), 577-583]
is a multiple temporal cluster detection approach, which is based on a data
transformation. The model selection procedure and the test of the cluster
significance are achieved by bootstrap. The use of simulations is a common point
between existing temporal cluster detection methods. The aim of this paper is to
propose a new approach to avoid the use of such simulations in the cluster
significance test stage. A direct application of the Bernstein inequality allows
to compute upper bounds for p-values for each potential cluster. We also
propose another model selection procedure based on multiple structural changes
developed by Bai and Perron [Econometrica 66 (1998), 47-78].
The new detection approach based on inequalities is detailed. Those inequalities
are applied on simulated data and on two real data sets. A discussion concludes
the paper. |
Keywords and phrases: cluster detection, Bernstein inequality, multiple structural changes, temporal cluster, break dates, double maximum test. |
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