A simulated annealing strategy for the detection of arbitrarily shaped spatial clusters

By L. Duczmal, Renato Martins Assunção. Computational Statistics & Data Analysis, 2004

304 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Tests of Spatial Randomness Adjusted for an Inhomogeneity (2006, methodology): “296), the Patil–Taillie upper-level set scan statistics (Patil and Taillie 2003, 2004), the simulated annealing scan statistic (Duczmal and Assunção 2004; Duczmal, Kulldorff, and Huang 2006), the Tango–Takahashi flexibly shaped scan statistic (Tango and Takahashi 2005), Assunção et al.”
Weighted Normal Spatial Scan Statistic for Heterogeneous Population Data (2009, background): “There exist a large number of spatial cluster detection methods for analyzing such individual data, depending on the nature of the data, such as dichotomous variables for prevalence of cases and controls, Poisson variables for incidence or mortality (Turnbull et al. 1990; Kulldorff 1997; Duczmal and”
Upper level set scan statistic for detecting arbitrarily shaped hotspots (2004, methodology): “For this reason, stochastic optimization methods will not be discussed further in this paper; however, Duczmal and Assuncao (2002) have applied simulated annealing to do global optimization for the scan statistic.”