抄録
Let {X(t)} be a stationary process with mean zero and spectral density g(x). We shall use a kth order parametric spectral model f τ(k) (x) for this process. Without Gaussianity we can obtain an estiamte of τ(k), say ĝt(k), by maximizing the quasi-Gaussian likelihood of this model. We can then construct the best linear predictor of X(t), which is computed on the basis of the estimated spectral density f ĝt(k) (x). An asymptotic lower bound of the mean square error of the estimated predictor is obtained. The bound is attained if k is selected by Akaike's information criterion.
本文言語 | English |
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ページ(範囲) | 401-419 |
ページ数 | 19 |
ジャーナル | Annals of the Institute of Statistical Mathematics |
巻 | 32 |
号 | 1 |
DOI | |
出版ステータス | Published - 1980 12月 1 |
外部発表 | はい |
ASJC Scopus subject areas
- 統計学および確率