School of Community Health Sciences, University of Nevada, Reno, NV, USA.
School of Nursing, The State University of New York, University at Buffalo, Buffalo, NY, USA.
Stat Methods Med Res. 2020 Jan;29(1):3-14. doi: 10.1177/0962280218820406. Epub 2018 Dec 28.
We propose a flexible and computationally efficient penalized estimation method for a semi-parametric linear transformation model with current status data. To facilitate model fitting, the unknown monotone function is approximated by monotone -splines, and a computationally efficient hybrid algorithm involving the Fisher scoring algorithm and the isotonic regression is developed. A goodness-of-fit test and model diagnostics are also considered. The asymptotic properties of the penalized estimators are established, including the optimal rate of convergence for the function estimator and the semi-parametric efficiency for the regression parameter estimators. An extensive numerical experiment is conducted to evaluate the finite-sample properties of the penalized estimators, and the methodology is further illustrated with two real studies.
我们提出了一种灵活且计算高效的惩罚估计方法，用于具有当前状态数据的半参数线性变换模型。为了便于模型拟合，未知单调函数通过单调样条进行近似，并且开发了一种涉及 Fisher 评分算法和单调回归的计算高效混合算法。还考虑了拟合优度检验和模型诊断。建立了惩罚估计量的渐近性质，包括函数估计量的最优收敛速度和回归参数估计量的半参数效率。进行了广泛的数值实验来评估惩罚估计量的有限样本性质，并通过两个实际研究进一步说明了该方法。
