Sequential Estimation for the Semiparametric Additive Hazard Model

摘要

In this chapter, we investigate the asymptotic behavior of the sequential version of the regression parameter estimator for the additive hazard model. We mainly establish that the Lin and Ying (1994) nonsequential estimator is strongly consistent (in the sense of complete convergence) and that this estimator, indexed by any regular sequence (sequential estimator), has the same asymptotic behavior as the nonsequential estimator. An example of a fixed-width confidence-type sequential estimator is illustrated by simulations.