Collaborative Research: Fence Methods for Complex Model Selection Problems

协作研究：复杂模型选择问题的栅栏方法

• 批准号: 1148545
• 负责人: Jonnagadda Rao
• 金额: $ 2.31万
• 依托单位: University of Miami School of Medicine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-02-28 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1148545&HistoricalAwards=false
• 关键词: Collaborative; Research; Fence; Methods; Complex

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 some conventional situations, such as regression and ARMA time series have been studied. Yet, such strategies do not always translate into good finite sample performance. Furthermore, such standard model selection procedures encounter difficulties for nonconventional model selection problems as well. This project aims at developments of a new model selection strategy, called fence methods, in following four major areas of methodology research and applications: (i) development of adaptive fence methods for high dimensional and complex model selection problems using the idea of restricted maximum likelihood; (ii) development of data adaptive fence methods for nonparametric model selection 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 implementing the fence methods. The fence idea is generally based on building a statistical fence, or barrier, to carefully eliminate incorrect models. This is done by determining which models are within variation of a goodness-of-fit measure of an anchor model. Once the fence is constructed, the optimal model is selected from amongst those within the fence according to a criterion which can be made flexible. For example, the criterion can incorporate scientific or economic concerns. The adaptive fence method may be viewed as comparing signals with noises to come out with an optimal decision supported by the data. Given such a wide spectrum of models that can be handled, the range of applications seems enormous. Of particular interests are applications in human genetics, medical research and surveys. To facilitate such translational research, the investigators plan to freely disseminate available computer software to implement the fence methods.

许多模型搜索策略涉及在惩罚的拟合优度度量中权衡模型拟合与模型复杂性。这些类型的程序在一些传统的情况下，如回归和阿尔马时间序列的渐近性质进行了研究。然而，这种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遇到的困难，以及非传统的模型选择问题。本计画旨在发展一种新的模型选择策略，称为栅栏法，其方法学研究与应用的四个主要领域为：（i）利用限制极大似然的思想发展高维复杂模型选择问题的自适应栅栏法，（ii）发展非参数模型选择问题的数据自适应栅栏法，如惩罚光滑样条估计，（iii）发展非参数模型选择问题的数据自适应栅栏法，（iv）发展非参数模型选择问题的数据自适应栅栏法，（v）发展非参数模型选择问题的数据自适应栅栏法。（iii）发展数量性状基因座（QTL）定位的栅栏方法;及（iv）发展方便用户的独立软件，以实施栅栏方法。栅栏的想法通常是基于建立一个统计栅栏或障碍，以仔细消除不正确的模型。这通过确定哪些模型在锚模型的拟合优度度量的变化内来完成。一旦构造了围栏，就根据可以变得灵活的标准从围栏内的那些模型中选择最优模型。例如，该标准可以包括科学或经济方面的考虑。自适应栅栏方法可以被视为将信号与噪声进行比较，以得出由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、医学研究和调查中的应用。为了促进这种转化研究，研究人员计划免费传播可用的计算机软件来实现围栏方法。