Céline Lévy-Leduc

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We propose a semiparametric approach to fundamental frequency estimation of an unknown periodic signal in additive white noise based on model selection. Our estimator maximizes a penalized version of the cumulated periodogram and is proved to be consistent and asymptotically efficient under very general conditions. When the number of observations is fixed,(More)
A desirable property of an autocovariance estimator is to be robust to the presence of additive outliers. It is well-known that the sample autocovariance, being based on moments, does not have this property. Hence, the use of an autocovariance estimator which is robust to additive outliers can be very useful for time-series modeling. In this paper , the(More)
This paper suggests a robust estimation procedure for the parameters of the periodic AR (PAR) models when the data contains additive outliers. The proposed robust methodology is an extension of the robust scale and covariance functions given in, respectively, Rousseeuw and Croux [29] and Ma and Genton [24], to accommodate periodicity. These periodic robust(More)
We propose a novel approach for distributed statistical detection of change-points in high-volume network traffic. We consider more specifically the task of detecting and identifying the targets of Distributed Denial of Service (DDoS) attacks. The proposed algorithm, called DTopRank, performs distributed network anomaly detection by aggregating the partial(More)
Detecting and locating changes in highly multivariate data is a major concern in several current statistical applications. In this context, the first contribution of the paper is a novel non-parametric two-sample homogeneity test for multivariate data based on the well-known Wilcoxon rank statistic. The proposed two-sample homogeneity test statistic can be(More)
MOTIVATION The spatial conformation of the chromosome has a deep influence on gene regulation and expression. Hi-C technology allows the evaluation of the spatial proximity between any pair of loci along the genome. It results in a data matrix where blocks corresponding to (self-)interacting regions appear. The delimitation of such blocks is critical to(More)
We propose a non-parametric statistical procedure for detecting multiple change-points in multidimensional signals. The method is based on a test statistic that generalizes the well-known Kruskal-Wallis procedure to the multivariate setting. The proposed approach does not require any knowledge about the distribution of the observations and is(More)
We introduce a novel statistical test for unsupervised detection of changepoints in multidimensional sequences of temporal observations. The test statistic is based on a multivariate generalization of the Mann-Whitney Wilcoxon two-sample test. The proposed test performs nonparametric changepoint localization and returns a quantifiable measure of(More)