Title:
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The efficiency of estimates in stationary autoregressive series (English) |
Author:
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Anděl, Jiří |
Language:
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English |
Journal:
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Aplikace matematiky |
ISSN:
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0373-6725 |
Volume:
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15 |
Issue:
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1 |
Year:
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1970 |
Pages:
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18-30 |
Summary lang:
|
English |
Summary lang:
|
Czech |
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Category:
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math |
. |
Summary:
|
Let $X_1,\ldots,X_N$ be a finite random sequence with the expectation $EX_t=\alpha\varphi_t(1\leq t\leq N)$ and with the regular covariance matrix $\bold G$. The matrix $\bold G$ and the values of $\varphi_t$ are supposed to be known; $\alpha$ is an unknown parameter. The least squares estimate $\hat{\alpha}$ and the best linear unbiased estimate (BLUE) $\tilde{\alpha}$ of the parameter $\alpha$ are mentioned. The efficiency $\ell_N=var\ \hat{\alpha}/var\ \tilde{\alpha}$ is derived. The exact value of $\ell_N$ is given for cases when $X_1,\ldots,X_N$ is a finite part of the autoregressive series of the first and of the second order and $\varphi_t\equiv 1$ and $\varphi_t =t\ (1 \leq t\leq N)$ and for the autoregressive series of the $n$-th order with $\varphi_t\equiv 1$. The efficiency and the asymptotic efficiency of the BLUE $\tilde{\alpha}$ in cases when $\bold G$ is not true covariance matrix is also considered. () |
MSC:
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62F10 |
MSC:
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62M10 |
idZBL:
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Zbl 0205.46204 |
idMR:
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MR0258216 |
DOI:
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10.21136/AM.1970.103264 |
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Date available:
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2008-05-20T17:46:54Z |
Last updated:
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2020-07-28 |
Stable URL:
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http://hdl.handle.net/10338.dmlcz/103264 |
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Reference:
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[1] U. Grenander M. Rosenblatt: Statistical analysis of stationary time series.New York, 1957. MR 0084975 |
Reference:
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[2] J. Hájek: On linear statistical problems in stochastic processes.Czech. Math. J. 12 (87), 1962, 404-444. MR 0152090 |
Reference:
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[3] E. J. Hannan: Анализ временных рядов.Москва 1964. Zbl 0116.11402 |
Reference:
|
[4] T. A. Magness J. В. McGuire: Comparison of least squares and minimum variance estimates of regression parameters.Ann. Math. Stat. 33, 1962, 462-470. MR 0141201, 10.1214/aoms/1177704573 |
Reference:
|
[5] G. S. Watson: Linear least squares regression.Ann. Math. Stat. 38, 1967, 1679-1699. Zbl 0155.26801, MR 0219206, 10.1214/aoms/1177698603 |
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