In this chapter, we present two approaches for clustering spatial functional data. The first one is the model-based that uses concept of density random variables. second hierarchical based on univariate statistics data such as mode or mean. These take into account features data: observations are spatially close and share a common distribution associated methodologies illustrated by an applicati...

Extended Abstract. Inferences for spatial data are affected substantially by the spatial configuration of the network of sites where measurements are taken. Consider the following standard data-model framework for spatial data. Suppose a continuous, spatially-varying quantity, Z, is to be observed at a predetermined number, n, of points ....[ To Countinue Click here]

the statistical analysis of spatial data is usually done under gaussian assumption for the underlying random field model. when this assumption is not satisfied, block bootstrap methods can be used to analyze spatial data. one of the crucial problems in this setting is specifying the block sizes. in this paper, we present asymptotic optimal block size for separate block bootstrap to estimate the...