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Estimation for Nonnegative First-Order Autoregressive Processes with an Unknown Location Parameter

DOI: 10.4236/am.2012.312A294, PP. 2133-2147

Keywords: Nonnegative Time Series, Autoregressive Processes, Extreme Value Estimator, Regular Variation, Point Processes

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Abstract:

Consider a first-order autoregressive processes \"\", where the innovations are nonnegative random variables with regular variation at both the right endpoint infinity and the unknown left endpoint θ. We propose estimates for the autocorrelation parameter f and the unknown location parameter θ by taking the ratio of two sample values chosen with respect to an extreme value criteria for f and by taking the minimum of \"\" over the observed series, where \"\" represents our estimate for f. The joint limit distribution of the proposed estimators is derived using point process techniques. A simulation study is provided to examine the small sample size behavior of these estimates.

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