The Annals of Statistics
- Ann. Statist.
- Volume 33, Number 2 (2005), 501-521.
Likelihood approach for marginal proportional hazards regression in the presence of dependent censoring
In many public health problems, an important goal is to identify the effect of some treatment/intervention on the risk of failure for the whole population. A marginal proportional hazards regression model is often used to analyze such an effect. When dependent censoring is explained by many auxiliary covariates, we utilize two working models to condense high-dimensional covariates to achieve dimension reduction. Then the estimator of the treatment effect is obtained by maximizing a pseudo-likelihood function over a sieve space. Such an estimator is shown to be consistent and asymptotically normal when either of the two working models is correct; additionally, when both working models are correct, its asymptotic variance is the same as the semiparametric efficiency bound.
Ann. Statist., Volume 33, Number 2 (2005), 501-521.
First available in Project Euclid: 26 May 2005
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Zeng, Donglin. Likelihood approach for marginal proportional hazards regression in the presence of dependent censoring. Ann. Statist. 33 (2005), no. 2, 501--521. doi:10.1214/009053604000001291. https://projecteuclid.org/euclid.aos/1117114326