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Title: Robustness of the best linear unbiased estimator and predictor in linear regression models (English)
Author: Štulajter, František
Language: English
Journal: Aplikace matematiky
ISSN: 0373-6725
Volume: 35
Issue: 2
Year: 1990
Pages: 162-168
Summary lang: English
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Category: math
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Summary: If is shown that in linear regression models we do not make a great mistake if we substitute some sufficiently precise approximations for the unknown covariance matrix and covariance vector in the expressions for computation of the best linear unbiased estimator and predictor. (English)
Keyword: linear regression model
Keyword: mean integrated square error
Keyword: the best linear unbiased estimator and predictor
Keyword: robustness
Keyword: covariance matrix
MSC: 62F35
MSC: 62J05
MSC: 62M20
idZBL: Zbl 0704.62049
idMR: MR1042852
DOI: 10.21136/AM.1990.104398
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Date available: 2008-05-20T18:38:56Z
Last updated: 2020-07-28
Stable URL: http://hdl.handle.net/10338.dmlcz/104398
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Reference: [1] E. Parzen: Time series analysis papers.Holden - Day, San Francisco 1967. Zbl 0171.39602, MR 0223042
Reference: [2] C. R. Rao: Linear statistical inference and its applications.Wiley, New-York 1965. Zbl 0137.36203, MR 0221616
Reference: [3] O. N. Strand: Coefficient errors caused by using the wrong covariance matrix in the general linear regression model.Ann. Stat. (2), 1974, 935-949. MR 0356378, 10.1214/aos/1176342815
Reference: [4] F. Štulajter: Estimators with minimal mean integrated square error in regression models.Submitted to Statistics.
Reference: [5] F. Štulajter: Estimation in random processes.SNTL - Alfa, Bratislava (to appear in 1989).
Reference: [6] B. Z. Vulich: An introduction to functional analysis.(Russian). Nauka, Moscow 1967. MR 0218864
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