Dimitris N. Politis

Efstathios Paparoditis15
Tucker Mcelroy13
15Efstathios Paparoditis
13Tucker Mcelroy
10Timothy L Mcmurry
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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)
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 autore-gressive representation with respect to(More)
We extend earlier work on the NoVaS transformation approach introduced by Poli-tis (2003a,b). The proposed approach is model-free and especially relevant when making forecasts in the context of model uncertainty and structural breaks. We introduce a new implied distribution in the context of NoVaS , a number of additional methods for implementing NoVaS and(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 order to construct prediction intervals without the cumbersome—and typically unjustifiable—assumption of Gaussianity, some form of resampling is necessary. The regression setup has been well-studied in the literature but time series prediction faces additional difficulties. The paper at hand focuses on time series that can be modeled as linear, nonlinear(More)
We discuss the state-of-the-art of methodological and practical developments for resampling inference techniques in situations where either the data and/or the parameters being estimated take values in a space of functions. These problems have been investigated from the perspective of non-parametric smoothing, purely functional data, and empirical(More)