A hypothesis-free multiple scan statistic with variable window.
In this article we propose a new technique for identifying clusters in temporal point processes. This relies on the comparision between all the m -order spacings and it is totally independent of any alternative hypothesis. A recursive procedure is introduced and allows to identify multiple clusters independently. This new scan statistic seems to be more efficient than the classical scan statistic for detecting and recovering cluster alternatives. These results have applications in epidemiological studies of rare diseases.
Cucala
L
L
Institut de Mathématiques de Toulouse, Université Paul Sabatier, 31062 Toulouse Cedex 9, France. cucala@cict.fr
D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D004933:Esophageal Atresia / Q000453:epidemiology; D006801:Humans; D007822:Laryngeal Neoplasms / Q000453:epidemiology; D008175:Lung Neoplasms / Q000453:epidemiology; D014138:Tracheoesophageal Fistula / Q000151:congenital / Q000453:epidemiology