部分线性变换模型的惩罚对数似然估计
Penalized log-likelihood estimation for partly linear transformation models with current status data
摘要：
我们考虑部分线性变换模型应用于当前状态数据。未知量是转换函数、线性回归参数和非参数回归效应。结果表明，惩罚的最大似然估计的回归参数是渐近正态和有效的，并收敛于参数率，虽然惩罚的最大似然估计的转换功能和非参数回归效应是只有$n^{1/3}$一致。研究了基于块刀切法的回归参数推断问题。我们还研究了计算问题，并通过模拟研究证明了所提出的方法。转换模型和部分线性回归条款，加上新的估计和推理技术，提供了灵活的替代Cox模型的现状数据分析。
英文摘要：
We consider partly linear transformation models applied to current status data. The unknown quantities are the transformation function, a linear regression parameter and a nonparametric regression effect. It is shown that the penalized MLE for the regression parameter is asymptotically normal and efficient and converges at the parametric rate, although the penalized MLE for the transformation function and nonparametric regression effect are only $n^{1/3}$ consistent. Inference for the regression parameter based on a block jackknife is investigated. We also study computational issues and demonstrate the proposed methodology with a simulation study. The transformation models and partly linear regression terms, coupled with new estimation and inference techniques, provide flexible alternatives to the Cox model for current status data analysis.
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