MISSING OBSERVATIONS IN MULTIVARIATE STATISTICS--IV A NOTE ON SIMPLE LINEAR REGRESSION.

In this note we examine the bias and small sample efficiency of certain estimators for the parameters of a linear regression function when some observations are missing. The estimators studies in this paper were suggested by the large sample study reported in this issue of the Journal. We conclude that our asymptotically unbiased estimators of beta and mu[sub y|x], have negligible bias in sample sizes as small as n = 20, and that our asymptotically unbiased estimator of sigma[sup 2] may have an 8% bias when n = 20. The small sample and asymptotic efficiencies of these estimators are nearly the same for n = 60; when n = 20 the difference between these two efficiencies depends on the correlation coefficient rho and the pattern of missing observations. [ABSTRACT FROM AUTHOR]