Fence methods for mixed model selection
混合模型选择的围栏方法AI 解读 ↓
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混合模型选择的围栏方法
- 研究问题
- 当混合效应模型中样本量难以简单定义时，如何构造模型选择程序，兼顾模型拟合、复杂度和选择一致性？
- 方法与思路
- 作者先构造统计围栏排除错误模型，再按可灵活设定的准则在围栏内选择，并提出逐步搜索与自适应调参变体。
- 材料陈述的贡献
- 摘要称方法覆盖线性及广义线性混合模型，给出基本方法和变体一致性的充分条件，并用模拟和真实数据说明。
- 阅读关注点
- 应核对正确模型与最优模型的定义、围栏构造和调参方式，以及一致性所需的渐近框架与候选模型限制。
- 阅读边界
- 索引摘要未注明稿件版本，未给出一致性条件或实证比较数值；混合效应模型的应用不能自动视为面板数据结果。
原文摘要
Many model search strategies involve trading off model fit with model complexity in a penalized goodness of fit measure. Asymptotic properties for these types of procedures in settings like linear regression and ARMA time series have been studied, but these do not naturally extend to nonstandard situations such as mixed effects models, where simple definition of the sample size is not meaningful. This paper introduces a new class of strategies, known as fence methods, for mixed model selection, which includes linear and generalized linear mixed models. The idea involves a procedure to isolate a subgroup of what are known as correct models (of which the optimal model is a member). This is accomplished by constructing a statistical fence, or barrier, to carefully eliminate incorrect models. Once the fence is constructed, the optimal model is selected from among those within the fence according to a criterion which can be made flexible. In addition, we propose two variations of the fence. The first is a stepwise procedure to handle situations of many predictors; the second is an adaptive approach for choosing a tuning constant. We give sufficient conditions for consistency of fence and its variations, a desirable property for a good model selection procedure. The methods are illustrated through simulation studies and real data analysis.