估计员
数学
半参数回归
协变量
半参数模型
转化(遗传学)
非参数统计
参数统计
渐近分布
加性模型
推论
统计
估计方程
准似然
应用数学
计数数据
计算机科学
人工智能
基因
化学
泊松分布
生物化学
出处
期刊:Biometrika
[Oxford University Press]
日期:2006-09-01
卷期号:93 (3): 627-640
被引量:185
标识
DOI:10.1093/biomet/93.3.627
摘要
A class of semiparametric transformation models is proposed to characterise the effects of possibly time-varying covariates on the intensity functions of counting processes. The class includes the proportional intensity model and linear transformation models as special cases. Nonparametric maximum likelihood estimators are developed for the regression parameters and cumulative intensity functions of these models based on censored data. The estimators are shown to be consistent and asymptotically normal. The limiting variances for the estimators of the regression parameters achieve the semi-parametric efficient bounds and can be consistently estimated. The limiting variances for the estimators of smooth functionals of the cumulative intensity function can also be consistently estimated. Simulation studies reveal that the proposed inference procedures perform well in practical settings. Two medical studies are provided.
科研通智能强力驱动
Strongly Powered by AbleSci AI