| 11. |
Comment |
| |
Journal of the American Statistical Association,
Volume 72,
Issue 360,
1977,
Page 808-810
GeorgeA. Barnard,
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ISSN:0162-1459
DOI:10.1080/01621459.1977.10479957
出版商:Taylor & Francis Group
年代:1977
数据来源: Taylor
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| 12. |
Comment |
| |
Journal of the American Statistical Association,
Volume 72,
Issue 360,
1977,
Page 810-812
LawrenceD. Brown,
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ISSN:0162-1459
DOI:10.1080/01621459.1977.10479958
出版商:Taylor & Francis Group
年代:1977
数据来源: Taylor
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| 13. |
Comment |
| |
Journal of the American Statistical Association,
Volume 72,
Issue 360,
1977,
Page 813-814
RobertJ. Buehler,
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ISSN:0162-1459
DOI:10.1080/01621459.1977.10479959
出版商:Taylor & Francis Group
年代:1977
数据来源: Taylor
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| 14. |
Comment |
| |
Journal of the American Statistical Association,
Volume 72,
Issue 360,
1977,
Page 814-816
ArthurP. Dempster,
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ISSN:0162-1459
DOI:10.1080/01621459.1977.10479960
出版商:Taylor & Francis Group
年代:1977
数据来源: Taylor
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| 15. |
Comment |
| |
Journal of the American Statistical Association,
Volume 72,
Issue 360,
1977,
Page 816-819
Oscar Kempthorne,
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ISSN:0162-1459
DOI:10.1080/01621459.1977.10479961
出版商:Taylor & Francis Group
年代:1977
数据来源: Taylor
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| 16. |
Comment |
| |
Journal of the American Statistical Association,
Volume 72,
Issue 360,
1977,
Page 819-820
DennisV. Lindley,
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ISSN:0162-1459
DOI:10.1080/01621459.1977.10479962
出版商:Taylor & Francis Group
年代:1977
数据来源: Taylor
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| 17. |
Comment |
| |
Journal of the American Statistical Association,
Volume 72,
Issue 360,
1977,
Page 820-822
J. Wolfowitz,
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ISSN:0162-1459
DOI:10.1080/01621459.1977.10479963
出版商:Taylor & Francis Group
年代:1977
数据来源: Taylor
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| 18. |
Rejoinder |
| |
Journal of the American Statistical Association,
Volume 72,
Issue 360,
1977,
Page 822-827
J. Kiefer,
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ISSN:0162-1459
DOI:10.1080/01621459.1977.10479964
出版商:Taylor & Francis Group
年代:1977
数据来源: Taylor
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| 19. |
Two Robust Alternatives to Least-Squares Regression |
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Journal of the American Statistical Association,
Volume 72,
Issue 360,
1977,
Page 828-833
RichardW. Hill,
PaulW. Holland,
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摘要:
We give Monte Carlo results on the performance of two robust alternatives to least-squares regression estimation—least-absolute residuals and the one-step sine estimator. We show how to scale the residuals for the sine estimator to achieve nearly constant efficiency for the normal distribution across various choices of the design matrix. We compare the two estimators to least squares for nine scale-contaminated normal distributions.
ISSN:0162-1459
DOI:10.1080/01621459.1977.10479965
出版商:Taylor & Francis Group
年代:1977
数据来源: Taylor
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| 20. |
The Estimation of the Prediction Error Variance |
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Journal of the American Statistical Association,
Volume 72,
Issue 360,
1977,
Page 834-840
E.J. Hannan,
D.F. Nicholls,
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摘要:
Spectral methods are used to construct an estimate of the variance of the prediction error for a normal, stationary process. The estimate obtained is shown to be strongly consistent and asymptotically normally distributed. Some aspects of the computations with respect to the fast Fourier transform are considered. The latter half of the article consists of a number of simulations, based on both generated and real data, which illustrate the results obtained. The relation between the estimate and that obtained from a high order autoregression is discussed.
ISSN:0162-1459
DOI:10.1080/01621459.1977.10479966
出版商:Taylor & Francis Group
年代:1977
数据来源: Taylor
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