Two bootstrap strategies for a \(k\)-problem up to location-scale with dependent samples
Summary: This paper extends the authors' work [Comput. Stat. Data Anal. 56, No. 6, 2097--2111 (2012; Zbl 1243.62063)] who considered tests for the \(k\)-sample problem with dependent samples. Here, the marginal distributions are allowed, under \(\mathcal{H}_0\), to differ according to their mean and their variance; in other words, one focuses on the shape of the distributions. Although easily stated, this problem nevertheless requires a careful treatment for the computation of valid \(P\) values. To this end, two bootstrap strategies based on the multiplier central limit theorem are proposed, both exploiting a representation of the test statistics in terms of a Hadamard differentiable functional. This accounts for the fact that one works with empirically standardized data instead of the original observations. Simulations reported show the nice sample properties of the method based on Cramér-von Mises and characteristic function type statistics. The newly introduced tests are illustrated on the marginal distributions of the eight-dimensional \textit{Oil currency} data set.
scientific article; zbMATH DE number 6928178