聚焦信息准则
Focused Information Criteria
AI总结：
聚焦信息准则针对特定研究关注量选择最优统计模型，适用于多种模型类型，还可扩展至高维数据等场景，为统计模型与变量选择提供了特定聚焦导向的方案。
AI中文摘要：
聚焦信息准则用于在多个统计模型或模型中需纳入的多个变量间进行选择。与其他同类信息准则不同，它旨在为给定的研究问题关注量（即聚焦参数）选择最优模型，不同的聚焦参数可能导致不同的模型选择，每个模型对相应聚焦参数而言最优。“最优”由风险函数定义，通常为均方误差，也可考虑其他风险用于聚焦选择。该准则的适用选择范围包括参数（广义）线性模型、非参数与半参数模型、分位数回归模型、图模型、生存数据模型、纵向数据模型、时间序列模型等多种模型；其基础版本的扩展包括适用于高维数据、正则化估计及贝叶斯方法的版本。
英文摘要：
The focused information criterion is used to make a choice among several statistical models, or among several variables to include in a model. Different from other such information criteria, the focused information criterion is constructed to select the best model for a given interest quantity, the focus of the research question. Different such focus parameters may lead to different selected models, each one best for the corresponding focus. What is `best' is defined by a risk function, often the mean squared error. Other risks can be considered too for focused selection. Selections by the focused information criterion include using parametric (generalized) linear models, non- and semiparametric models, quantile regression models, graphical models, models for survival data, for longitudinal data, time series models, and many more. Extensions of the basic version include versions for high-dimensional data, regularized estimation, and Bayesian methods.
