标准生存模型（例如比例风险模型）包含与风险规模相对应的单个回归组件。相反，我们考虑所谓的“多参数回归”方法，其中协变量同时通过多个分布参数（例如尺度和形状参数）进入模型。这种方法之前已被证明可以以相对较低的模型复杂性实现灵活性。然而，除了逐步类型选择方法之外，变量选择方法在多参数回归生存建模设置中还不够发达。因此，我们提出使用以下惩罚的惩罚多参数回归估计程序：最小绝对收缩和选择算子、平滑剪切绝对偏差以及自适应最小绝对收缩和选择算子。我们使用广泛的模拟研究和对肺癌观察研究数据的应用来比较这些程序；威布尔多参数回归模型自始至终用作运行示例。

Standard survival models such as the proportional hazards model contain a single regression component, corresponding to the scale of the hazard. In contrast, we consider the so-called "multi-parameter regression" approach whereby covariates enter the model through multiple distributional parameters simultaneously, for example, scale and shape parameters. This approach has previously been shown to achieve flexibility with relatively low model complexity. However, beyond a stepwise type selection method, variable selection methods are underdeveloped in the multi-parameter regression survival modeling setting. Therefore, we propose penalized multi-parameter regression estimation procedures using the following penalties: least absolute shrinkage and selection operator, smoothly clipped absolute deviation, and adaptive least absolute shrinkage and selection operator. We compare these procedures using extensive simulation studies and an application to data from an observational lung cancer study; the Weibull multi-parameter regression model is used throughout as a running example.