Spatial median and directional data

We introduce the normalized spatial median as an estimator of location for rotationally symmetric distributions on the hypersphere. We investigate some of its asymptotic properties and use them to obtain confidence regions for the modal direction of a distribution on the hypersphere. These results are then applied to the von Mises-Fisher distribution and to a contamination model. It is seen that the normalized spatial median can perform more efficiently than the normalized mean in presence of outlier

Association of Vasculitis and Familial Mediterranean Fever

Certain types of vasculitis occur more frequently and present differently in patients with familial Mediterranean fever (FMF). We assessed the characteristics of patients with FMF and systemic vasculitis through a systematic review of the literature. Medline was searched by two independent investigators until December 2017. We screened 310 articles and selected 58 of them (IgA vasculitis n = 12, polyarteritis nodosa (PAN) n = 25, Behçet's disease (BD) n = 7, other vasculitis n = 14). Clinical case reports were available for 167 patients (IgA vasculitis n = 46, PAN n = 61, BD n = 46, other vasculitis n = 14), and unavailable for 45 patients (IgA vasculitis n = 38, PAN n = 7). IgA vasculitis was the most common vasculitis in FMF patients with a prevalence of 2.7–7%, followed by PAN with a prevalence of 0.9–1.4%. Characteristics of FMF did not differ between patients with and without vasculitis. Patients with FMF and IgA vasculitis displayed more intussusception (8.7%) and possibly less IgA deposits on histological analysis than patients with IgA vasculitis alone. Patients with FMF and PAN had a younger age at vasculitis onset (mean age = 17.9 years), as well as more perirenal hematomas (49%) and CNS involvement (31%) than patients with PAN alone. Glomerular involvement was noted in 33% of patients diagnosed with PAN, suggesting an alternative diagnosis. Sequencing of the MEFV gene confirmed the presence of two pathogenic variants in 73% of FMF patients with IgA vasculitis or PAN. The majority of patients with BD were from one case series, and presented more skin, gastrointestinal, and CNS involvement than patients with isolated BD. In conclusion, FMF, particularly when supported by two pathogenic MEFV mutations, could predispose to IgA vasculitis, or a PAN-like vasculitis with more perirenal bleeding and CNS involvement

Analysis Of Variance and CPA in SCA

This paper introduces Side-Channel Analysis results obtained on an unprotected circuit characterized by a surprisingly non-linear leakage. While in such a case, Correlation Power Analysis is not adapted, we show that a more generic attack, based on the Analysis Of Variance (AOV) outperfoms CPA. It has the advantage of detecting non-linear leakage, unlike Correlation Power Analysis, and of providing similar or much better results in all cases, with a similar computation time

Consistent selection of the actual model in regression analysis

In regression analysis, a best subset of regressors is usually selected by minimizing Mallows's C statistic or some other equivalent criterion, such as the Akaike lambda information criterion or cross-validation. It is known that the resulting procedure suffers from a lack of consistency that can lead to a model with too many variables. For this reason, corrections have been proposed that yield consistent procedures. The object of this paper is to show that these corrected criteria, although asymptotically consistent, are usually too conservative for finite sample sizes. The paper also proposes a new correction of Mallows's statistic that yields better results. A simulation study is conducted that shows that the proposed criterion performs well in a variety of situations.

Critères de qualité d'un classifieur généraliste

This paper considers the problem of choosing a good classifier. For each problem there exist an optimal classifier, but none are optimal, regarding the error rate, in all cases. Because there exists a large number of classifiers, a user would rather prefer an all-purpose classifier that is easy to adjust, in the hope that it will do almost as good as the optimal. In this paper we establish a list of criteria that a good generalist classifier should satisfy. We first discuss data analytic, these criteria are presented. Six among the most popular classifiers are selected and scored according to these criteria. Tables allow to easily appreciate the relative values of each. In the end, random forests turn out to be the best classifiers.-Cet article considère le problème de choisir un bon classifieur. Pour chaque contexte il existe un classifieur optimal selon le critère du taux d'erreur, mais aucun n'est optimal dans tous les cas. Comme il existe de nombreux classi-fieurs, lÕutilisateur préférera souvent choisir un classifieur généraliste, dont l'ajustement et l'exploitation sont à sa portée, en espérant que celui-ci fait presque aussi bien que l'optimal. Cet article établit une liste de critères que devrait rencontrer un bon classifieur généraliste, destiné à être ajusté et utilisé avec un minimum d'intervention humaine. Après avoir introduit l'analytique des données, ces critères sont présentés et commentés. Puis un sous-ensemble de six classifieurs est choisi parmi les plus populaires et des scores leur sont attribués en regard de ces critères. Des tables permettent d'apprécier les résul-tats et facilitent le choix d'un bon classifieur. Le classifieur qui ressort de cet exercice avec les meilleurs scores est la forêt aléatoire et ses variantes.léatoire (random forest) et ses variantes

Uniqueness of the least-distances estimator in regression models with multivariate response

International audienc

Critères de qualité d'un classifieur généraliste

This paper considers the problem of choosing a good classifier. For each problem there exist an optimal classifier, but none are optimal, regarding the error rate, in all cases. Because there exists a large number of classifiers, a user would rather prefer an all-purpose classifier that is easy to adjust, in the hope that it will do almost as good as the optimal. In this paper we establish a list of criteria that a good generalist classifier should satisfy. We first discuss data analytic, these criteria are presented. Six among the most popular classifiers are selected and scored according to these criteria. Tables allow to easily appreciate the relative values of each. In the end, random forests turn out to be the best classifiers.-Cet article considère le problème de choisir un bon classifieur. Pour chaque contexte il existe un classifieur optimal selon le critère du taux d'erreur, mais aucun n'est optimal dans tous les cas. Comme il existe de nombreux classi-fieurs, lÕutilisateur préférera souvent choisir un classifieur généraliste, dont l'ajustement et l'exploitation sont à sa portée, en espérant que celui-ci fait presque aussi bien que l'optimal. Cet article établit une liste de critères que devrait rencontrer un bon classifieur généraliste, destiné à être ajusté et utilisé avec un minimum d'intervention humaine. Après avoir introduit l'analytique des données, ces critères sont présentés et commentés. Puis un sous-ensemble de six classifieurs est choisi parmi les plus populaires et des scores leur sont attribués en regard de ces critères. Des tables permettent d'apprécier les résul-tats et facilitent le choix d'un bon classifieur. Le classifieur qui ressort de cet exercice avec les meilleurs scores est la forêt aléatoire et ses variantes.léatoire (random forest) et ses variantes