Fence Methods for Complex Model Selection Problems

许多模型搜索策略涉及在受惩罚的拟合优度度量中权衡模型匹配与模型复杂性。研究了这类过程在回归和ARMA时间序列等常规情形下的渐近性质。然而，这样的策略并不总是转化为良好的有限样本性能。此外，这种标准的模型选择程序对于非常规模型选择问题也会遇到困难。本项目的目标是开发一种新的模型选择策略，称为FARE方法，在方法学研究和应用的四个主要领域：(I)利用约束最大似然的思想开发高维和复杂模型选择问题的自适应FARK方法；(Ii)开发用于非参数模型选择问题(如惩罚平滑样条估计)的数据自适应FARK方法；(Iii)开发用于数量性状基因座(QTL)定位的FARK方法；以及(Iv)开发用户友好的独立软件来实现FARK方法。FARCH的思想通常基于建立统计围栏或屏障，以小心地消除不正确的模型。这是通过确定哪些模型在锚模型的拟合优度的变化范围内来完成的。一旦围栏建成，就可以根据一个可以变得灵活的标准从围栏内的模型中选择最优的模型。例如，标准可以包含科学或经济方面的考虑。自适应栅栏方法可以看作是将信号与噪声进行比较，以得出数据支持的最优决策。考虑到可以处理的模型范围如此之广，应用范围似乎很大。特别感兴趣的是在人类遗传学、医学研究和测量方面的应用。为了促进这类翻译研究，研究人员计划免费传播可用的计算机软件来实施FRICE方法。

Many model search strategies involve trading off model fit with modelcomplexity in a penalized goodness of fit measure. Asymptotic propertiesfor these types of procedures in some conventional situations, such asregression and ARMA time series have been studied. Yet, such strategiesdo not always translate into good finite sample performance. Furthermore,such standard model selection procedures encounter difficulties fornonconventional model selection problems as well. This project aims atdevelopments of a new model selection strategy, called fence methods, infollowing four major areas of methodology research and applications: (i)development of adaptive fence methods for high dimensional and complexmodel selection problems using the idea of restricted maximum likelihood;(ii) development of data adaptive fence methods for nonparametric modelselection problems such as penalized smoothing spline estimation; (iii)development of fence methods for quantitative trait loci (QTL) mapping;and (iv) development of user-friendly standalone software for implementingthe fence methods.The fence idea is generally based on building a statistical fence, orbarrier, to carefully eliminate incorrect models. This is done bydetermining which models are within variation of a goodness-of-fitmeasure of an anchor model. Once the fence is constructed, the optimalmodel is selected from amongst those within the fence according toa criterion which can be made flexible. For example, the criterion canincorporate scientific or economic concerns. The adaptive fence methodmay be viewed as comparing signals with noises to come out with an optimaldecision supported by the data. Given such a wide spectrum of models thatcan be handled, the range of applications seems enormous. Of particularinterests are applications in human genetics, medical research and surveys.To facilitate such translational research, the investigators plan to freelydisseminate available computer software to implement the fence methods.