Large-sample properties for a general estimator of the treatment effect in the two-sample problem with right censoring. (English) Zbl 0747.62046

Meng, Xiao-Li; Bassiakos, Yiannis; Lo, Shaw-Hwa

Ann. Stat. 19, No. 4, 1786-1812 (1991).

This paper considers the problem of estimation of shift in location when comparing two samples subjected to right random censoring. The estimators are based on the Kaplan-Meier estimator of the d.f.s. The proposed estimator is shown to be strongly consistent and asymptotically normal.
A novel feature of the paper is the fact that an estimate of the asymptotic variance of the estimator can be obtained without involving the derivative of the density. The paper also presents results on oscillation of the Kaplan-Meier process as well as the associated quantile process.

Reviewer: B.K.Kale (Pune)

MSC:

62G20	Asymptotic properties of nonparametric inference
62G05	Nonparametric estimation

Keywords:

estimation of treatment effects; two-sample problem; right censoring; survival analysis; location shift model; scale change model; large-sample properties; generalized Hodges-Lehmann type estimator; asymptotic normality; strong consistency; oscillation behavior of the Kaplan-Meier process