Using Triples to Assess Symmetry Under Weak Dependence
研究如何利用基于数据三元组的U统计量，在弱依赖条件下检验严格平稳随机过程一维边际分布关于未知中心的对称性，并提出了基于子抽样的推断方法和数据驱动的子样本大小选择规则。
The problem of assessing symmetry about an unspecified center of the one-dimensional marginal distribution of strictly stationary random processes is considered. A well-known U-statistic based on data triples is used to detect deviations from symmetry, allowing the underying process to satisfy suitable mixing or near-epoch dependence conditions. We suggest using subsampling for inference on the target parameter, establish the asymptotic validity of the method in our setting, and discuss data-driven rules for selecting the size of subsamples. The small-sample properties of the proposed inference procedures are examined by means of Monte Carlo simulations and an application to time series of real output growth is also presented.