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We apply some new change point detection methods to a series of data relative to annual climatic anomalies, and to three series of data relative to the first wave of the Covid-19 in France in 2020. For each of t hese s eries, many change-points are detected and their locations are estimated.
The study of copulas and their applications in statistics, and in particular in biostatistics, is recent. Copulas play a important role in the modeling of the dependence structure between the marginal distributions and the joint distribution of a vector random variable. In this article, we present a synthesis of recent works on this theory and its applications to the analysis of multivariate survival data. Finally, an application on bivariate survival data from the literature analyzed by the Proc Copula procedure of the SAS software, is given to illustrate a such approach.
This paper aims at establishing the asymptotic normality for a kernel conditional quantile estimator in a right censorship model for which, the lifetime observations and the covariates are assumed to satisfy an association dependency type.