From a series of observations Y1,…,Yn\documentclass[12pt]{minimal}
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\begin{document}$${\mathbf Y}_1, \ldots , {\mathbf Y}_n$$\end{document} in Rd\documentclass[12pt]{minimal}
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\begin{document}$$\mathbb {R}^d$$\end{document} taken sequentially, an interesting question is to know whether or not a significant change occurred in their stochastic behavior. The problem has been largely investigated both for univariate and multivariate observations, where the null hypothesis states that F1=⋯=Fn\documentclass[12pt]{minimal}
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\begin{document}$$F_1 = \cdots = F_n$$\end{document}, where Fj(y)=P(Yj≤y)\documentclass[12pt]{minimal}
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\begin{document}$$F_j({\mathbf y}) = \mathrm{P}({\mathbf Y}_j \le {\mathbf y})$$\end{document}. In most of the works done so far, the alternative hypothesis is generally that of an abrupt change at some unknown time K, i.e. Fj=D1\documentclass[12pt]{minimal}
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\begin{document}$$F_j = D_1$$\end{document} for j≤K\documentclass[12pt]{minimal}
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\begin{document}$$j \le K$$\end{document} and Fj=D2\documentclass[12pt]{minimal}
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\begin{document}$$F_j = D_2$$\end{document} when j>K\documentclass[12pt]{minimal}
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\begin{document}$$j > K$$\end{document}. This assumption is unrealistic in applications where changes tend to occur gradually. In this paper, a more general gradual-change model is proposed in which one admits the existence of times K1<K2\documentclass[12pt]{minimal}
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\begin{document}$$K_1 < K_2$$\end{document} where the distribution smoothly changes from D1\documentclass[12pt]{minimal}
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\begin{document}$$D_1$$\end{document} to D2\documentclass[12pt]{minimal}
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\begin{document}$$D_2$$\end{document}. A general class of consistent test statistics for the detection of gradual changes is introduced and their large-sample behavior is investigated under a general α\documentclass[12pt]{minimal}
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\begin{document}$$\alpha $$\end{document}-mixing condition. The proposed framework allows to detect changes in the marginal series as well as in the copula. Monte-Carlo simulations indicate the good sampling properties of the tests and their usefulness is illustrated on climatic data.
