arXiv subjects

Mohamed-Salem Ahmed

Publications and source records attributed to Mohamed-Salem Ahmed.

6 recordsLinked to original sources

arXiv2022

k-nearest neighbors prediction and classification for spatial data

This paper proposes a spatial k-nearest neighbor method for nonparametric prediction of real-valued spatial data and supervised classification for categorical spatial data. The proposed method is based on a double nearest neighbor rule which combines two kernels to control the distances between observations and locations. It uses a random bandwidth in order to more appropriately fit the distributions of the covariates. The almost complete convergence with rate of the proposed predictor is established and the almost sure convergence of the supervised classification rule was deduced. Finite sample properties are given for two applications of the k-nearest neighbor prediction and classification rule to the soil and the fisheries datasets

math.ST↗

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.

stat.ME↗

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).

stat.ME↗

arXiv2019

Spatial Autoregressive Models for Scan Statistic

Spatial scan statistics are well-known methods for cluster detection and are widely used in epidemiology and medical studies for detecting and evaluating the statistical significance of disease hotspots. For the sake of simplicity, the classical spatial scan statistic assumes that the observations of the outcome variable in different locations are independent, while in practice the data may exhibit a spatial correlation. In this article, we use spatial autoregressive (SAR) models to account the spatial correlation in parametric/non-parametric scan statistic. Firstly, the correlation parameter is estimated in the SAR model to transform the outcome into a new independent outcome over all locations. Secondly, we propose an adapted spatial scan statistic based on this independent outcome for cluster detection. A simulation study highlights the better performance of the proposed methods than the classical one in presence of spatial correlation in the data. The latter shows a sharp increase in Type I error and false-positive rate but also decreases the true-positive rate when spatial correlation increases. Besides, our methods retain the Type I error and have stable true and false positive rates with respect to the spatial correlation. The proposed methods are illustrated using a spatial economic dataset of the median income in Paris city. In this application, we show that taking spatial correlation into account leads to the identification of more concentrated clusters than those identified by the classical spatial scan statistic.

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arXiv2019

A functional-model-adjusted spatial scan statistic

This paper introduces a new spatial scan statistic designed to adjust cluster detection for longitudinal confounding factors indexed in space. The functional-model-adjusted statistic was developed using generalized functional linear models in which longitudinal confounding factors were considered to be functional covariates. A general framework was developed for application to various probability models. Application to a Poisson model showed that the new method is equivalent to a conventional spatial scan statistic that adjusts the underlying population for covariates. In a simulation study with univariate and multivariate models, we found that our new method adjusts the cluster detection procedure more accurately than other methods. Use of the new spatial scan statistic was illustrated by analysing data on premature mortality in France over the period from 1998 to 2013, with the quarterly unemployment rate as a longitudinal confounding factor.

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