Abstract
This paper presents a novel class of semiparametric estimating functions for the additive model with right-censored data that are obtained from general biased-sampling. The new estimator can be obtained using a weighted estimating equation for the covariate coeffcients, by embedding the biased-sampling data into left-truncated and right-censored data. The asymptotic properties (consistency and asymptotic normality) of the proposed estimator are derived via the modern empirical processes theory. Based on the cumulative residual processes, we also propose graphical and numerical methods to assess the adequacy of the additive risk model. The good finite-sample performance of the proposed estimator is demonstrated by simulation studies and two applications of real datasets.
Original language | English |
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Pages (from-to) | 1495-1518 |
Number of pages | 24 |
Journal | Science China Mathematics |
Volume | 61 |
Issue number | 8 |
DOIs | |
Publication status | Published - Aug 2018 |
Keywords
- 62N02
- 62N03
- additive risk model
- biased-sampling data
- estimating equation
- missing covariates
- model checking
ASJC Scopus subject areas
- General Mathematics