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


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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A. Bartlett and W. McCormick, "Estimation for Nonnegative First-Order Autoregressive Processes with an Unknown Location Parameter," Applied Mathematics, Vol. 3 No. 12A, 2012, pp. 2133-2147. doi: 10.4236/am.2012.312A294.

Conflicts of Interest

The authors declare no conflicts of interest.


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