Qin J, Wang M C
Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York 10021, USA.
Lifetime Data Anal. 2001 Sep;7(3):225-42. doi: 10.1023/a:1011632323888.
Randomly truncated data are frequently encountered in many studies where truncation arises as a result of the sampling design. In the literature, nonparametric and semiparametric methods have been proposed to estimate parameters in one-sample models. This paper considers a semiparametric model and develops an efficient method for the estimation of unknown parameters. The model assumes that K populations have a common probability distribution but the populations are observed subject to different truncation mechanisms. Semiparametric likelihood estimation is studied and the corresponding inferences are derived for both parametric and nonparametric components in the model. The method can also be applied to two-sample problems to test the difference of lifetime distributions. Simulation results and a real data analysis are presented to illustrate the methods.
在许多因抽样设计而出现截断的研究中，经常会遇到随机截断数据。在文献中，已经提出了非参数和半参数方法来估计单样本模型中的参数。本文考虑一个半参数模型，并开发一种有效的方法来估计未知参数。该模型假设K个总体具有共同的概率分布，但总体是在不同的截断机制下进行观测的。研究了半参数似然估计，并针对模型中的参数和非参数成分得出了相应的推断。该方法也可应用于双样本问题以检验寿命分布的差异。给出了模拟结果和实际数据分析以说明这些方法。
