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Robustness of the best linear unbiased estimator and predictor in linear regression models
Applications of Mathematics, Tome 35 (1990) no. 2, pp. 162-168
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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.
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.
DOI : 10.21136/AM.1990.104398
Classification : 62F35, 62J05, 62M20
Keywords: linear regression model; mean integrated square error; the best linear unbiased estimator and predictor; robustness; covariance matrix 
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Štulajter, František. Robustness of the best linear unbiased estimator and predictor in linear regression models. Applications of Mathematics, Tome 35 (1990) no. 2, pp. 162-168. doi: 10.21136/AM.1990.104398

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[4] F. Štulajter: Estimators with minimal mean integrated square error in regression models. Submitted to Statistics.

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