On equivalence problem in linear regression models. I. BLUE of the mean value

Gejza Wimmer

Aplikace matematiky (1980)

  • Volume: 25, Issue: 6, page 417-422
  • ISSN: 0862-7940

Abstract

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There exist many different ways of determining the best linear unbiased estimation of regression coefficients in general regression model. In Part I of this article it is shown that all these ways are numerically equivalent almost everyvhere. In Part II conditions are considered under which all the unbiased estimations of the unknown covariance matrix scalar factor are numerically equivalent almost everywhere.

How to cite

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Wimmer, Gejza. "On equivalence problem in linear regression models. I. BLUE of the mean value." Aplikace matematiky 25.6 (1980): 417-422. <http://eudml.org/doc/15168>.

@article{Wimmer1980,
abstract = {There exist many different ways of determining the best linear unbiased estimation of regression coefficients in general regression model. In Part I of this article it is shown that all these ways are numerically equivalent almost everyvhere. In Part II conditions are considered under which all the unbiased estimations of the unknown covariance matrix scalar factor are numerically equivalent almost everywhere.},
author = {Wimmer, Gejza},
journal = {Aplikace matematiky},
keywords = {control; numerical stability; best linear unbiased estimation; unknown covariance matrix scalar factor; control; numerical stability; best linear unbiased estimation; unknown covariance matrix scalar factor},
language = {eng},
number = {6},
pages = {417-422},
publisher = {Institute of Mathematics, Academy of Sciences of the Czech Republic},
title = {On equivalence problem in linear regression models. I. BLUE of the mean value},
url = {http://eudml.org/doc/15168},
volume = {25},
year = {1980},
}

TY - JOUR
AU - Wimmer, Gejza
TI - On equivalence problem in linear regression models. I. BLUE of the mean value
JO - Aplikace matematiky
PY - 1980
PB - Institute of Mathematics, Academy of Sciences of the Czech Republic
VL - 25
IS - 6
SP - 417
EP - 422
AB - There exist many different ways of determining the best linear unbiased estimation of regression coefficients in general regression model. In Part I of this article it is shown that all these ways are numerically equivalent almost everyvhere. In Part II conditions are considered under which all the unbiased estimations of the unknown covariance matrix scalar factor are numerically equivalent almost everywhere.
LA - eng
KW - control; numerical stability; best linear unbiased estimation; unknown covariance matrix scalar factor; control; numerical stability; best linear unbiased estimation; unknown covariance matrix scalar factor
UR - http://eudml.org/doc/15168
ER -

References

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  1. C. R. Rao, Unified Theory of Linear Estimation, Sankhyā, Vol. 33 1971, pp. 371 - 394. (1971) Zbl0236.62048MR0319321
  2. C. R. Rao, Linear Statistical Inference and Its Applications, John Wiley, N. York 1973. (1973) Zbl0256.62002MR0346957
  3. C. R. Rao S. K. Mitra, Generalized Inverse of Matrices and Its Applications, John Wiley, N. York 1971. (1971) MR0338013
  4. C. R. Rao, Corrigenda, Sankhyā A, Vol. 34 1972 p. 477. (1972) Zbl0261.62051MR0347011

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