作者：ELSTER, C; NEUMAIER, A
作者单位：University of Vienna
摘要：Screening experiments aim to identify the relevant variables within some process potentially depending on a large number of variables. In this paper we introduce a new class of experimental designs called edge designs. These designs allow a model-independent estimate of the set of relevant variables, thus providing more robustness than traditional designs. We give a bound on the determinant of the information matrix of certain edge designs, and show that a large class of edge designs meeting t...
作者：HALL, P; HOROWITZ, JL; JING, BY
作者单位：University of Iowa; Hong Kong University of Science & Technology
摘要：We address the issue of optimal block choice in applications of the block bootstrap to dependent data. It is shown that optimal block size depends significantly on context, being equal to n(1/3), n(1/4) and n(1/5) in the cases of variance or bias estimation, estimation of a one-sided distribution function, and estimation of a two-sided distribution function, respectively. A clear intuitive explanation of this phenomenon is given, together with outlines of theoretical arguments in specific case...
