Linear programming estimators and bootstrapping for heavy tailed phenomena

Linear programming estimators and bootstrapping for heavy tailed phenomena

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Article ID: iaor19983124
Country: United Kingdom
Volume: 29
Issue: 3
Start Page Number: 759
End Page Number: 805
Publication Date: Sep 1997
Journal: Advances in Applied Probability
Authors: ,
Keywords: programming: linear
Abstract:

For autoregressive time series with positive innovations which either have heavy right or left tails, linear programming parameter estimates of the autoregressive coefficients have good rates of convergence. However, the asymptotic distribution of the estimators depends heavily on the distribution of the process and thus cannot be used for inference. A bootstrap procedure circumvents this difficulty. We verify the validity of the bootstrap and also give some general comments on the bootstrapping of heavy tailed phenomena.

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