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Camille Frévent

Publications and source records attributed to Camille Frévent.

9 recordsLinked to original sources
arXiv2026

A spatial scan statistical for categorical, functional data

We have developed and tested a spatial scan statistic for categorical, functional data (CFSS) - a data structure within which current approaches cannot identify spatial clusters. Our methodology combines an encoding scheme for categorical, functional observations with a nonparametric scan statistic. In a simulation study with three distinct scenarios, the CFSS accurately recovered the simulated spatial clusters and gave very low false positive rates, high true positive rates, and high positive predictive values. We have also used the CFSS to identify and characterize spatial clusters in French air pollution data from the winter of 2024.

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arXiv2025

A signature-based spatial scan statistic for functional data

We have developed a new signature-based spatial scan statistic for functional data (SigFSS). This scan statistic can be applied to both univariate and multivariate functional data. In a simulation study, SigFSS almost always performed better than the literature approaches and yielded more precise clusters in geographic terms. Lastly, we used SigFSS to search for spatial clusters of abnormally high or abnormally low mortality rates in mainland France.

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arXiv2025

A multivariate spatial regression model using signatures

We propose a spatial autoregressive model for a multivariate response variable and functional covariates. The approach is based on the notion of signature, which represents a function as an infinite series of its iterated integrals and presents the advantage of being applicable to a wide range of processes. We have provided theoretical guarantees for the choice of the signature truncation order, and we have shown in a simulation study and an application to pollution data that this approach outperforms existing approaches in the literature.

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arXiv2024

Uncovering Data Across Continua: An Introduction to Functional Data Analysis

In a world increasingly awash with data, the need to extract meaningful insights from data has never been more crucial. Functional Data Analysis (FDA) goes beyond traditional data points, treating data as dynamic, continuous functions, capturing ever-changing phenomena nuances. This article introduces FDA, merging statistics with real-world complexity, ideal for those with mathematical skills but no FDA background.

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arXiv2024

A functional spatial autoregressive model using signatures

We propose a new approach to the autoregressive spatial functional model, based on the notion of signature, which represents a function as an infinite series of its iterated integrals. It presents the advantage of being applicable to a wide range of processes. After having provided theoretical guarantees to the proposed model, we have shown in a simulation study and on a real data set that this new approach presents competitive performances compared to the traditional model.

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arXiv2022

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 detection of spatial clusters of events. In the field of spatial analysis of time-to-event data, several models of scan statistics have been proposed. However, these models do not take into account the potential intra-unit spatial correlation of individuals nor a potential correlation between spatial units. To overcome this problem, we propose here a scan statistic based on a Cox model with shared frailty that takes into account the spatial correlation between spatial units. In simulation studies, we have shown that (i) classical models of spatial scan statistics for time-to-event data fail to maintain the type I error in the presence of intra-spatial unit correlation, and (ii) our model performs well in the presence of both intra-spatial unit correlation and inter-spatial unit correlation. Our method has been applied to epidemiological data and to the detection of spatial clusters of mortality in patients with end-stage renal disease in northern France.

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arXiv2021

Investigating spatial scan statistics for multivariate functional data

This paper introduces new scan statistics for multivariate functional data indexed in space. The new methods are derivated from a MANOVA test statistic for functional data, an adaptation of the Hotelling T2-test statistic, and a multivariate extension of the Wilcoxon rank-sum test statistic. In a simulation study, the latter two methods present very good performances and the adaptation of the functional MANOVA also shows good performances for a normal distribution. Our methods detect more accurate spatial clusters than an existing nonparametric functional scan statistic. Lastly we applied the methods on multivariate functional data to search for spatial clusters of abnormal daily concentrations of air pollutants in the north of France in May and June 2020.

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arXiv2021

Detecting spatial clusters in functional data: new scan statistic approaches

We have developed two scan statistics for detecting clusters of functional data indexed in space. The first method is based on an adaptation of a functional analysis of variance and the second one is based on a distribution-free spatial scan statistic for univariate data. In a simulation study, the distribution-free method always performed better than a nonparametric functional scan statistic, and the adaptation of the anova also performed better for data with a normal or a quasi-normal distribution. Our methods can detect smaller spatial clusters than the nonparametric method. Lastly, we used our scan statistics for functional data to search for spatial clusters of abnormal unemployment rates in France over the period 1998-2013 (divided into quarters).

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