Robustness of the best linear unbiased estimator and predictor in linear regression models

František Štulajter

Aplikace matematiky (1990)

  • Volume: 35, Issue: 2, page 162-168
  • ISSN: 0862-7940

Abstract

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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.

How to cite

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Štulajter, František. "Robustness of the best linear unbiased estimator and predictor in linear regression models." Aplikace matematiky 35.2 (1990): 162-168. <http://eudml.org/doc/15620>.

@article{Štulajter1990,
abstract = {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.},
author = {Štulajter, František},
journal = {Aplikace matematiky},
keywords = {linear regression model; mean integrated square error; the best linear unbiased estimator and predictor; robustness; covariance matrix; mean integrated square error; best linear unbiased predictor; robustness; covariance matrix; best linear unbiased estimator},
language = {eng},
number = {2},
pages = {162-168},
publisher = {Institute of Mathematics, Academy of Sciences of the Czech Republic},
title = {Robustness of the best linear unbiased estimator and predictor in linear regression models},
url = {http://eudml.org/doc/15620},
volume = {35},
year = {1990},
}

TY - JOUR
AU - Štulajter, František
TI - Robustness of the best linear unbiased estimator and predictor in linear regression models
JO - Aplikace matematiky
PY - 1990
PB - Institute of Mathematics, Academy of Sciences of the Czech Republic
VL - 35
IS - 2
SP - 162
EP - 168
AB - 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.
LA - eng
KW - linear regression model; mean integrated square error; the best linear unbiased estimator and predictor; robustness; covariance matrix; mean integrated square error; best linear unbiased predictor; robustness; covariance matrix; best linear unbiased estimator
UR - http://eudml.org/doc/15620
ER -

References

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  1. E. Parzen, Time series analysis papers, Holden - Day, San Francisco 1967. (1967) Zbl0171.39602MR0223042
  2. C. R. Rao, Linear statistical inference and its applications, Wiley, New-York 1965. (1965) Zbl0137.36203MR0221616
  3. O. N. Strand, 10.1214/aos/1176342815, Ann. Stat. (2), 1974, 935-949. (1974) Zbl0293.15022MR0356378DOI10.1214/aos/1176342815
  4. F. Štulajter, Estimators with minimal mean integrated square error in regression models, Submitted to Statistics. Zbl0671.62054
  5. F. Štulajter, Estimation in random processes, SNTL - Alfa, Bratislava (to appear in 1989). (1989) Zbl0698.62087
  6. B. Z. Vulich, An introduction to functional analysis, (Russian). Nauka, Moscow 1967. (1967) MR0218864

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