Generalized and oxdinary least squakes with estimated and unequal variances
- 1 January 1984
- journal article
- research article
- Published by Taylor & Francis in Communications in Statistics - Simulation and Computation
- Vol. 13 (4) , 521-537
- https://doi.org/10.1080/03610918408812394
Abstract
An assumption which is often violated in the application of experimental designs is equality of variances. There are several methods available for estimating the unequal variances. This paper covers incorporating different estimators of the variances with the ordinary least squares and generalized least squares. A Monte Carlo study provides more insight into the behavior of these procedures. For some small sample sizes, the incorporations with the ordinary least squares perform satisfactorily, but with the generalized least squares they do not.Keywords
This publication has 13 references indexed in Scilit:
- Regression Analysis for Simulation PractitionersJournal of the Operational Research Society, 1981
- Pseudorandom Number Assignment in Statistically Designed Simulation and Distribution Sampling ExperimentsJournal of the American Statistical Association, 1978
- Comparison of Estimators of Heteroscedastic Variances in Linear ModelsJournal of the American Statistical Association, 1975
- Estimating Heteroscedastic Variances in Linear ModelsJournal of the American Statistical Association, 1975
- On the Estimation of Heteroscedastic VariancesPublished by JSTOR ,1973
- Necessary and Sufficient Conditions for MINQU-Estimation of Heteroskedastic Variances in Linear ModelsJournal of the American Statistical Association, 1972
- Estimation of Variance and Covariance Components in Linear ModelsJournal of the American Statistical Association, 1972
- Combining Independent Estimators and Estimation in Linear Regression with Unequal VariancesPublished by JSTOR ,1971
- Estimation of variance and covariance components—MINQUE theoryJournal of Multivariate Analysis, 1971
- Estimation of Heteroscedastic Variances in Linear ModelsJournal of the American Statistical Association, 1970