DeepAI AI Chat
Log In Sign Up

Detecting spatial clusters on functional data: a parametric scan statistic approach

by   Camille Frévent, et al.

This paper proposes a parametric scan statistic for detecting clusters of functional data indexed in space. The proposed method is based on an adaptation of a functional ANOVA. In a simulation study, it presents better performances than the nonparametric functional scan statistic for normal data and good performances also on quasi-normal distributions. The proposed method allows to detect smaller spatial clusters than the nonparametric one. It shows also better performances than the parametric univariate spatial scan statistic applied on the average over time. The parametric scan statistic for functional data was then applied on the search of spatial clusters of abnormal unemployment rates in France, considering the period from 1998 to 2013 divided in quarters.


page 8

page 9


Investigating spatial scan statistics for multivariate functional data

This paper introduces new scan statistics for multivariate functional da...

A functional-model-adjusted spatial scan statistic

This paper introduces a new spatial scan statistic designed to adjust cl...

An expectation-based space-time scan statistic for ZIP-distributed data

An expectation-based scan statistic is proposed for the prospective moni...

Spatial Autoregressive Models for Scan Statistic

Spatial scan statistics are well-known methods for cluster detection and...

Spatial Multiresolution Cluster Detection Method

A novel multi-resolution cluster detection (MCD) method is proposed to i...

On the Asymptotic Distribution of the Scan Statistic for Point Clouds

We derive the large-sample distribution of several variants of the scan ...

A Bayesian shared-frailty spatial scan statistic model for time-to-event data

Spatial scan statistics are well known and widely used methods for the d...