Additive–multiplicative hazards model with current status data

Wanrong Liu, Jianglin Fang and Xuewen Lu ()
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Wanrong Liu: Hunan Normal University
Jianglin Fang: Hunan Normal University
Xuewen Lu: Hunan Normal University

Computational Statistics, 2018, vol. 33, issue 3, No 8, 1245-1266

Abstract: Abstract The additive–multiplicative hazards (AMH) regression model specifies an additive and multiplicative form on the hazard function for the counting process associated with a multidimensional covariate process, which contains the Cox proportional hazards model and the additive hazards model as its special cases. In this paper, we study the AMH model with current status data, where the cumulative hazard hazard function is assumed to be nonparametric and is estimated using B-splines with monotonicity constraint on the functional, while a simultaneous sieve maximum likelihood estimation is proposed to estimate regression parameters. The proposed estimator for the parameter vector is shown to be asymptotically normal and semiparametric efficient. The B-splines estimator of the functional of the cumulative hazard function is shown to achieve the optimal nonparametric rate of convergence. A simulation study is conducted to examine the finite sample performance of the proposed estimators and algorithm, and a real data example is presented for illustration.

Keywords: B-splines; Counting process; Current status data; Empirical process; Monotonicity constraint; Semiparametric efficiency bound (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s00180-018-0806-y

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