The Annals of Statistics

Likelihood approach for marginal proportional hazards regression in the presence of dependent censoring

Donglin Zeng

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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.

Article information

Ann. Statist., Volume 33, Number 2 (2005), 501-521.

First available in Project Euclid: 26 May 2005

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Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G07: Density estimation
Secondary: 62F12: Asymptotic properties of estimators

Semiparametric inference dimension reduction B-spline double robustness


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.

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