||Efficiency of Least Squares Estimators in the Presence of Spatial Autocorrelation.
Cordy, C. B. ;
Griffith, D. A. ;
||Oregon State Univ., Corvallis. Dept. of Statistics. ;Syracuse Univ., NY.;Corvallis Environmental Research Lab., OR.
Least squares method ;
Statistical inference ;
Numerical solution ;
Parameter estimation ;
||Some EPA libraries have a fiche copy filed under the call number shown.
The authors consider the effect of spatial autocorrelation on inferences made using ordinary least squares estimation. It is found, in some cases, that ordinary least squares estimators provide a reasonable alternative to the estimated generalized least squares estimators recommended in the spatial statistics literature. One of the most serious problems in using ordinary least squares is that the usual variance estimators are severely biased when the errors are correlated. An alternative variance estimator that adjusts for any observed correlation is proposed. The need to take autocorrelation into account in variance estimation negates much of the advantage that ordinary least squares estimation has in terms of computation simplicity. (Copyright (c) 1993 by Marcel Dekker, Inc.)