Center for Applied Statistical Research, School of Mathematics, Jilin University, Changchun, China.
School of Mathematical Sciences, Capital Normal University, Beijing, China.
Stat Med. 2019 May 30;38(12):2219-2227. doi: 10.1002/sim.8106. Epub 2019 Jan 30.
Semiparametric probit models have recently attracted some attention for regression analysis of failure time data partly due to the popularity of the normal distribution and its special features. In this paper, we discuss the fitting of such models to informative current status data, which often occur in many areas such as medical studies and whose analysis has also recently attracted a lot of attention. For inference, a sieve maximum likelihood approach is developed and the methodology is further generalized to a class of generalized semiparametric probit models. A simulation study is conducted to assess the finite sample properties of the presented approach and indicates that it works well in practical situations. An application that motivated this study is provided.
半参数概率模型最近由于正态分布及其特殊性质的流行而受到回归分析中失败时间数据的关注。在本文中,我们讨论了将此类模型拟合到信息性当前状态数据,这些数据经常出现在医学研究等许多领域,其分析也最近引起了很多关注。为了进行推理,开发了一种筛最大似然方法,并将该方法进一步推广到一类广义半参数概率模型。进行了模拟研究以评估所提出方法的有限样本性质,并表明它在实际情况下效果良好。提供了一个激发这项研究的应用。