ASYMPTOTIC NORMALITY OF A CLASS OF ADAPTIVE STATISTICS WITH APPLICATIONS TO SYNTHETIC DATA METHODS FOR CENSORED REGRESSION

Motivated by regression analysis of censored survival data, we develop herein a general asymptotic distribution theory for estimators define d by estimating equations of the form Sigma(i=1)(n) xi(w(i), theta, G( n);) = 0, in which w(i) represents observed data, theta is an unknown parameter to be estimated, and G(n) represents an estimate of some unk nown underlying distribution. This general theory is used to establish asymptotic normality of synthetic least squares estimates in censored regression models and to evaluate the covariance matrices of the limi ting normal distributions. (C) 1995 Academic Press, Inc.