| dc.description.abstract | The existing statistical methods do not provide a satisfactory solution to determining the
spatial pattern in spatially referenced data, which is often required by research in many areas
including geology, agriculture, forestry, marine science and epidemiology for identifying the source
of the unusual environmental factors associated with a certain phenomenon. This work provides
a novel algorithm which can be used to delineate the boundary of an area of hot spots accurately
and e ciently. Our algorithm, rst of all, does not assume any pre-speci ed geometric shapes
for the change-curve. Secondly, the computation complexity by our novel algorithm for changecurve
detection is in the order of O(n2), which is much smaller than 2O(n2) required by the CUSP
algorithm proposed in M uller&Song [8] and Carlstein's [2] estimators. Furthermore, our novel
algorithm yields a consistent estimate of the change-curve as well as the underlying distribution
mean of observations in the regions. We also study the hypothesis test of the existence of the
change-curve in the presence of independence of the spatially referenced data. We then provide
some simulation studies as well as a real case study to compare our algorithm with the popular
boundary estimation method : Spatial scan statistic. | en_US |
