Abstract
The Kaplan-Meier estimator of a survival function is well known to be asymptotically efficient when cause of failure is always observed. It has been an open problem, however, to find an efficient estimator when failure indicators are missing at random. Lo showed that nonparametric maximum likelihood estimators are inconsistent, and this has led to several proposals of ad hoc estimators, none of which are efficient. We now introduce a sieved nonparametric maximum likelihood estimator, and show that it is efficient. Our approach is related to the estimation of a bivariate survival function from bivariate right-censored data.
| Original language | English |
|---|---|
| Pages (from-to) | 164-182 |
| Journal | Annals of Statistics |
| Volume | 26 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Feb 1998 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Research Keywords
- Bivariate censorship
- Incomplete data
- Influence curve
- Kaplan-Meier estimator
- Self-consistency