18 citations · 18 across the 5 of their papers we have counts for
7 papers
Convergence of the empirical two-sample -statistics with -mixing data
Herold Dehling, Davide Giraudo, Olimjon Sharipov
We consider the empirical two-sample -statistic with strictly -mixing strictly stationary data and inverstigate its convergence in Skorohod spaces. We then provide an applica…
Bootstrapping Covariance Operators of Functional Time Series
Olimjon Sh. Sharipov, Martin Wendler
For testing hypothesis on the covariance operator of functional time series, we suggest to use the full functional information and to avoid dimension reduction techniques. The limi…
Estimation of the variance of partial sums of dependent processes
Herold Dehling, Roland Fried, Olimjon Sh. Sharipov +2
We study subsampling estimators for the limit variance \[ σ^2=Var(X_1)+2 \sum_{k=2}^\infty Cov(X_1,X_k) \] of partial sums of a stationary stochastic process . We…
Sequential block bootstrap in a Hilbert space with application to change point analysis
Olimjon Sharipov, Johannes Tewes, Martin Wendler
A new test for structural changes in functional data is investigated. It is based on Hilbert space theory and critical values are deduced from bootstrap iterations. Thus a new func…
Bootstrap for dependent Hilbert space-valued random variables with application to von Mises statistics
H. Dehling, O. Sh. Sharipov, M. Wendler
Statistical methods for functional data are of interest for many applications. In this paper, we prove a central limit theorem for random variables taking their values in a Hilbert…
Normal Limits, Nonnormal Limits, and the Bootstrap for Quantiles of Dependent Data
O. Sh. Sharipov, M. Wendler
We will show under very weak conditions on differentiability and dependence that the central limit theorem for quantiles holds and that the block bootstrap is weakly consistent. Un…
Bootstrap for the Sample Mean and for U-Statistics of Mixing and Near Epoch Dependent Processes
Olimjon Sh. Sharipov, Martin Wendler
The validity of various bootstrapping methods has been proved for the sample mean of strongly mixing data. But in many applications, there appear nonlinear statistics of processes…