基于Tobit回归模型，使用成对融合惩罚的正则化方法，对具有异质性的左删失数据进行子群分析，实现了回归参数估计与子群识别的同步进行。通过引入一组新变量，将原优化问题转化为可以用交替方向乘子法求解的仅含等式约束的多变量优化问题。并且，将每一步迭代的目标函数中与损失相关的多变量函数，利用广义坐标下降算法转化为一组二次优化单变量函数。证明所提算法的收敛性，并建立所得参数估计量的大样本性质。模拟研究和实际数据分析表明所提方法具有良好性能。

We use pairwise fusion penalty regularization method， based on Tobit regression model， to perform subgroup analysis on left-censored data with heterogeneity， simultaneously estimating regression parameters and identifying subgroups. By introducing a set of new parameters， the original optimization problem is transformed into a multivariate optimization problem with equality constraints only that can be solved by alternating direction method of multipliers. Moreover， the multivariate function related to the loss in each iteration is transformed into a group of quadratic surrogate functions of single variable by generalized coordinate descent algorithm. We prove that the proposed algorithm is convergent， and establish the large sample properties of the obtained parameter estimators. Simulation studies and real data analysis show that the proposed method has good performance.