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As far back as the late 70s, the impact of a ordable, high-speed computers on the theory and practice of modern statistics was recognized by Efron [14] [15]. As a result, the bootstrap and other computer-intensive statistical methods (such as subsampling and the jackknife) have been developed extensively since that time, and now constitute very powerful… (More)

We review the different block bootstrap methods for time series, and present them in a unified framework. We then revisit a recent result of Lahiri [Lahiri, S. N. (1999b). Theoretical comparisons of block bootstrap methods, Ann. Statist. 27:386–404] comparing the different methods and give a corrected bound on their asymptotic relative efficiency; we also… (More)

In this paper, we consider the problem of bandwidth choice in the parallel settings of nonparametric kernel smoothed spectral density and probability density estimation. We propose a new class of `plug-in' type bandwidth estimators, and show their favorable asymptotic properties. The new estimators automatically adapt to the degree of underlying smoothness… (More)

Sparked by Efron’s seminal paper, the decade of the 1980s was a period of active research on bootstrap methods for independent data— mainly i.i.d. or regression set-ups. By contrast, in the 1990s much research was directed towards resampling dependent data, for example, time series and random fields. Consequently, the availability of valid nonparametric… (More)

A new class of large-sample covariance and spectral density matrix estimators is proposed based on the notion of flat-top kernels. The new estimators are shown to be higher-order accurate when higher-order accuracy is possible. A discussion on kernel choice is presented as well as a supporting finite-sample simulation. The problem of spectral estimation… (More)

A nonparametric, residual-based block bootstrap procedure is proposed in the context of testing for integrated (unit root) time series. The resampling procedure is based on weak assumptions on the dependence structure of the stationary process driving the random walk and successfully generates unit root integrated pseudo-series retaining the important… (More)

In spatial statistics the data typically consist of measurements of some quantity at irregularly scattered locations; in other words, the data form a realization of a marked point process. In this paper, we formulate subsampling estimators of the moments of general statistics computed from marked point process data, and establish their L2 consistency. The… (More)

- Efstathios Paparoditis, Dimitris N. Politis
- 2002

A nonparametric bootstrap procedure is proposed for stochastic processes which follow a general autoregressive structure. The procedure generates bootstrap replicates by locally resampling the original set of observations reproducing automatically its dependence properties. It avoids an initial nonparametric estimation of process characteristics in order to… (More)

- SIEVE BOOTSTRAP, DIMITRIS N. POLITIS, D. N. POLITIS
- 2011

We explore the limits of the autoregressive (AR) sieve bootstrap, and show that its applicability extends well beyond the realm of linear time series as has been previously thought. In particular, for appropriate statistics, the AR-sieve bootstrap is valid for stationary processes possessing a general Wold-type autoregressive representation with respect to… (More)

- Efstathios Paparoditis, Dimitris N. Politis

The paper investigates how the particular choice of residuals used in a bootstrap-based testing procedure a ects the properties of the test. The properties of the tests are investigated both under the null and under the alternative. It is shown that for non-pivotal test statistics, the method used to obtain residuals largely a ects the power behavior of the… (More)