AN ITERATED VERSION OF THE GAUSS-MARKOV THEOREM IN GENERALIZED LEAST SQUARES ESTIMATION
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- Toyooka Yasuyuki
- Iowa State University Faculty of Economics of Nagasaki University
Bibliographic Information
- Other Title
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- An Iterated Version on the Gauss-Markov Theorem in Generalized Least Squares Estimation
- Iterated Version on the Gauss Markov Th
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Abstract
In the general linear model with covariance structure, depending on an unknown parameter vector, it is shown that the greatest lower bound for the risk matrix of the generalized least squares estimator (GLSE) constructed with covariance structure estimated from the iterated residuals is that of the Gauss-Markov estimator. A sufficient condition for the existence and the unbiasedness of the GLSE based on iterated residuals is given. It is shown that the use of the iterated residuals does not improve the risk matrix of GLSE through terms of order n-2 relative to that of the two step estimator.
Journal
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- Journal of the Japan Statistical Society, Japanese Issue
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Journal of the Japan Statistical Society, Japanese Issue 17 (2), 129-136, 1987
Japan Statistical Society
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Keywords
Details 詳細情報について
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- CRID
- 1390282679413615232
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- NII Article ID
- 130003582428
- 40002988180
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- NII Book ID
- AA1105098X
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- ISSN
- 21891478
- 03895602
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- MRID
- 930404
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- NDL BIB ID
- 3163974
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- Text Lang
- en
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- Data Source
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- JaLC
- NDL
- CiNii Articles
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- Abstract License Flag
- Disallowed