With the popularity of high-dimensional data, model selection is of great importance in recent survival analysis. In a model selection context, an important research question is how to define the best model. To answer this, various model selection criteria have been proposed for defining the best model. The existing methods commonly use the $L_0$-norm penalization in order to measure the model complexity based on the number of parameters. However, due to the nonconvexity of the $L_0$-penalty, finding the best model via global optimization has been a challenging research subject in statistics and machine learning. In this paper, we propose a global optimization algorithm using a modification of the simulated annealing, which is a probabilistic search algorithm for the global optimum in statistical mechanics. The performance of the proposed method is examined via simulation study and real data analysis.

随着高维数据的普及，模型选择在最近的生存分析中变得非常重要。在模型选择环境中，一个重要的研究问题是如何定义最佳模型。为了回答这个问题，已经提出了各种模型选择标准来定义最佳模型。现有的方法通常使用${升}_0$-norm 惩罚，以根据参数的数量来衡量模型的复杂性。但是，由于不凸${升}_0$-惩罚，通过全局优化寻找最佳模型一直是统计学和机器学习中具有挑战性的研究课题。在本文中，我们提出了一种使用模拟退火改进的全局优化算法，这是统计力学中全局优化的概率搜索算法。通过仿真研究和实际数据分析来检验所提出方法的性能。