title: Jackknife estimators of variance for parameter estimates from estimating equations with applications to clustered survival data.
abstract: An estimate of a parameter vector beta is often obtained by setting a "score" vector equal to zero and solving for beta. Estimating equations of this type include maximum likelihood, quasi-likelihood (McCullagh, 1983, Annals of Statistics 11, 59-67), and generalized estimating equations (Liang and Zeger, 1986, Biometrika 73, 13-22). White (1982, Econometrica 50, 1-26) proposed a variance estimator for beta that is robust to model misspecification. We show that a "one-step" jackknife estimator of variance is asymptotically equivalent to the variance estimator proposed by White. The one-step variance estimator may be preferred when the appropriate computer packages are not available to compute White's estimator directly. This jackknife estimator is very useful in our example with clustered survival data.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115.
authors.forename: S R
authors.initials: SR
authors.lastname: Lipsitz
authors.forename: K B
authors.initials: KB
authors.lastname: Dear
authors.forename: L
authors.initials: L
authors.lastname: Zhao
mesh_terms: D000704:Analysis of Variance; D001699:Biometry; D016000:Cluster Analysis; D004812:Epidemiologic Methods; D006801:Humans; D015233:Models, Statistical; D011336:Probability; D016019:Survival Analysis; D015996:Survival Rate