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 weight...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 coefficients, 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.展开更多
基金supported by National Natural Science Foundation of China(Grant Nos.11771133 and 11401194)the Natural Science Foundation of Hunan Province of China(Grant No.2017JJ3021)+2 种基金Zhao’s work was supported by National Natural Science Foundation of China(Grant No.11771366)Zhou’s work was supported by the State Key Program of National Natural Science Foundation of China(Grant No.71331006)the State Key Program in the Major Research Plan of National Natural Science Foundation of China(Grant No.91546202)
文摘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 coefficients, 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.
