The Annals of Statistics

Semiparametric Estimation of Association in a Bivariate Survival Function

Gangaji Maguluri

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Abstract

Clayton's model for association in bivariate survival data is both of intrinsic importance and an interesting example of a semiparametric estimation problem, that is, a problem where inference about a parameter is required in the presence of nuisance functions. The joint distribution of the two survival times in this model is absolutely continuous and a single parameter governs the association between the two survival times. In this paper we describe an algorithm to derive the asymptotic lower bound for the information of the parameter governing the association. We discuss the construction of one-step estimators and compare their performance to that of other estimators in a Monte Carlo study.

Article information

Source
Ann. Statist., Volume 21, Number 3 (1993), 1648-1662.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176349277

Digital Object Identifier
doi:10.1214/aos/1176349277

Mathematical Reviews number (MathSciNet)
MR1241284

Zentralblatt MATH identifier
0792.62027

JSTOR
links.jstor.org

Subjects
Primary: 62G05: Estimation
Secondary: 62G20: Asymptotic properties

Keywords
Alternating projections efficient score information bounds operators

Citation

Maguluri, Gangaji. Semiparametric Estimation of Association in a Bivariate Survival Function. Ann. Statist. 21 (1993), no. 3, 1648--1662. doi:10.1214/aos/1176349277. https://projecteuclid.org/euclid.aos/1176349277


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