|
| 摘要: |
| 针对带有删失的生存数据，比例风险模型是应用最为广泛的半参数生存模型之一。在实际
应用中，比例风险模型回归参数的估计需要使用数值方法计算获得。近年来，随着机器学习与深度
学习领域研究的不断深入，自适应优化算法得到迅速发展。本文研究比例风险模型下的自适应优
化算法，首先我们探讨了三种自适应优化算法——Adam算法、RMSprop算法、Adagrad算法求解
比例风险模型下的参数估计数值解问题，展示自适应算法的计算优良性。然后，我们深入了比例风
险模型下的Adam算法的研究，发展了一种改进的Adam算法，进一步提高了算法的计算速度并展
现了其计算优势。 |
| 关键词: Adam算法, RMSprop算法, Adagrad算法,比例风险模型. |
| DOI： |
| 分类号:O212.2 |
| 基金项目: |
|
| Auto-adapted Optimization Algorithms and its improved algorithm for parameter maximum likelihood estimation under the Proportional Hazards Model |
|
linwenqiang
|
| Abstract: |
| For the survival data with censored data, the proportional hazards model is one of
the most widely used semiparametric survival models. In practical applications, the estimation of
the regression hazards model regression parameters needs to be obtained by numerical methods.
In recent years, with the deepening of research in the field of machine learning and deep learning,
the auto-adapted optimization algorithms have developed rapidly. This paper studies the auto-
adapted optimization algorithms under the proportional hazards model, Firstly, we explore and
apply three auto-adapted optimization algorithms—Adam algorithm, RMSprop algorithm and
Adagrad algorithm to solve the numerical solution problem of parameter estimation under the
proportional hazards model, and show the computational superiority of the auto-adapted algorithm.
Then, we go deep into the proportional hazards model. Under the research of Adam algorithm,
an improved Adam algorithm is developed, which further improves the calculation speed of the
algorithm and shows its computational advantages. |
| Key words: Adam, RMSprop, Adagrad, Proportional Hazards Model. |
