Bottai Matteo, Zhang Jiajia
Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA.
Biom J. 2010 Aug;52(4):487-503. doi: 10.1002/bimj.200900310.
We consider a regression model where the error term is assumed to follow a type of asymmetric Laplace distribution. We explore its use in the estimation of conditional quantiles of a continuous outcome variable given a set of covariates in the presence of random censoring. Censoring may depend on covariates. Estimation of the regression coefficients is carried out by maximizing a non-differentiable likelihood function. In the scenarios considered in a simulation study, the Laplace estimator showed correct coverage and shorter computation time than the alternative methods considered, some of which occasionally failed to converge. We illustrate the use of Laplace regression with an application to survival time in patients with small cell lung cancer.
我们考虑一个回归模型，其中假设误差项服从一种非对称拉普拉斯分布。我们探讨在存在随机删失的情况下，给定一组协变量时，该模型在连续结果变量条件分位数估计中的应用。删失可能取决于协变量。通过最大化一个不可微的似然函数来进行回归系数的估计。在模拟研究中考虑的情形下，拉普拉斯估计量显示出正确的覆盖率，并且与所考虑的其他方法相比计算时间更短，其中一些方法偶尔会无法收敛。我们通过应用于小细胞肺癌患者的生存时间来说明拉普拉斯回归的使用。
