The local bootstrap for Markov processes
Date
2002Source
Journal of Statistical Planning and InferenceVolume
108Issue
1-2Pages
301-328Google Scholar check
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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 generate the pseudo-time series and the bootstrap replicates mimic several of the properties of the original process. Applications of the procedure in nonlinear time-series analysis are considered and theoretically justified some simulated and real data examples are discussed. © 2002 Published by Elsevier Science B.V.