we derive the limit theory for this class of estimators. We find that subsampling is highly advantageous for estimators … subsampling is impotent, in the sense that subsampling has no effect on the asymptotic distribution. Perhaps surprisingly, for the … efficient smooth kernels, such as the Parzen kernel, we show that subsampling is harmful as it increases the asymptotic variance …

we derive the limit theory for this class of estimators. We find that subsampling is highly advantageous for estimators … subsampling is impotent, in the sense that subsampling has no effect on the asymptotic distribution. Perhaps surprisingly, for the … efficient smooth kernels, such as the Parzen kernel, we show that subsampling is harmful as it increases the asymptotic variance …

We compute the breakdown point of the subsampling quantile of a general statistic, and show that it is increasing in … the subsampling block size and the breakdown point of the statistic. These results imply fragile subsampling quantiles for … moderate block sizes, also when subsampling procedures are applied to robust statistics. This instability is inherited by data …

-values computed by bootstrap and subsampling schemes for the proposed test statistics. Empirical analysis based on data drawn from the …

parameter via a spectral representation and a subsampling approximation of the MISE. Finally, we provide an empirical …

—both the subsampling method and a plug-in approach. Simulation studies compare the size and power of both approaches as applied … to hypothesis testing for the mean. Both methods perform well–although the subsampling method appears to be better sized …

We study subsampling estimators for the limit variance σ2=V ar(X1)+2∑k=2∞Cov(X1,Xk) of partial sums of a stationary …

the consistency of subsampling procedure applied for the Fourier coefficient. Based on these results we propose a …