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Linear Programming Estimators and Bootstrapping for Heavy Tailed Phenomena
Published online by Cambridge University Press: 01 July 2016
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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MSC classification
- Type
- General Applied Probability
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- Copyright
- Copyright © Applied Probability Trust 1997
Footnotes
Research supported by US–Israel Binational Science Foundation (BSF) Grant No. 92-00227/2 and NSF Grant DMS-9400535.
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