Summary
In this paper we obtain an asymptotic expansion of the distribution of the maximum likelihood estimate (MLE)\(\hat \alpha _{ML} \) based onT observations from the first order Gaussian process up to the term of orderT −1. The expansion is used to compare\(\hat \alpha _{ML} \) with a generalized estimate\(\hat \alpha _{c_1 ,c_2 } \) including the least square estimate (LSE)\(\hat \alpha _{LS} \), based on the asymptotic probabilities around the true value of the estimates up to the terms of orderT −1. It is shown that\(\hat \alpha _{ML} \) (or the modified MLE\(\hat \alpha _{ML}^* \)) is better than\(\hat \alpha _{c_1 ,c_2 } \) (or the modified estimate\(\hat \alpha _{c_1 ,c_2 }^* \)). Further, we note that\(\hat \alpha _{ML}^* \) does not attain the bound for third order asymptotic median unbiased estimates.
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Fujikoshi, Y., Ochi, Y. Asymptotic properties of the maximum likelihood estimate in the first order autoregressive process. Ann Inst Stat Math 36, 119–128 (1984). https://doi.org/10.1007/BF02481958
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DOI: https://doi.org/10.1007/BF02481958