We consider strictly stationary stochastic processes of Hilbert space-valued random variables and focus on tests of the equality of the lag-zero autocovariance operators of several independent functional time series. A moving block bootstrap-based testing procedure is proposed which generates pseudo random elements that satisfy the null hypothesis of interest. It is based on directly bootstrapping the time series of tensor products which overcomes some common difficulties associated with applications of the bootstrap to related testing problems. The suggested methodology can be potentially applied to a broad range of test statistics of the hypotheses of interest. As an example, we establish validity for approximating the distribution under the null of a fully functional test statistic based on the Hilbert-Schmidt distance of the corresponding sample lag-zero autocovariance operators, and show consistency under the alternative. As a prerequisite, we prove a central limit theorem for the moving block bootstrap procedure applied to the sample autocovariance operator which is of interest on its own. The finite sample size and power performance of the suggested moving block bootstrap-based testing procedure is illustrated through simulations and an application to a real-life dataset is discussed.
We consider infinite-dimensional Hilbert space-valued random variables that are assumed to be temporal dependent in a broad sense. We prove a central limit theorem for the moving block bootstrap and for the tapered block bootstrap, and show that these block bootstrap procedures also provide consistent estimators of the long run covariance operator. Furthermore, we consider block bootstrap-based procedures for fully functional testing of the equality of mean functions between several independent functional time series. We establish validity of the block bootstrap methods in approximating the distribution of the statistic of interest under the null and show consistency of the block bootstrap-based tests under the alternative. The finite sample behaviour of the procedures is investigated by means of simulations. An application to a real-life dataset is also discussed. n )) − nE(X n ⊗ X n ) HS = o P (1), in probability.
The Tapered Block BootstrapThe TBB procedure is a modification of the block bootstrap procedure considered in Section 2.2 which is obtained by introducing a tapering of the random elements X t . The tapering function downweights the endpoints of each block B i , towards zero, i.e., towards the mean function of X t . The pseudo observations are then obtained by choosing, with replacement, k appropriately scaled and tapered blocks of length b of centered observations and joining them together.
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