Nonparametric and Semiparametric Modelling for Data Analysis

数据分析的非参数和半参数建模

• 批准号: 9971602
• 负责人: Hans-Georg Mueller
• 金额: $ 12万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-08-15 至 2002-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9971602&HistoricalAwards=false
• 关键词: Nonparametric; Semiparametric; Modelling; Data; Analysis

For many scientific problems, nonparametric and semiparametric statistical modelling can contribute to a better understanding and analysis. This includes the application of curve estimation techniques. The P.I. explores models which combine smooth (nonparametric), non-smooth (discontinuous) and parametric components. For high-dimensional data, such as curve data and longitudinal data, a sensible modelling approach includes components which achieve a dimension reduction. These are typically parametric parts of a model, and nonparametric smoothers are then applied in a second step to the dimension reduced data. The P.I. develops models which are geared towards scientific problems which yield data in the form of samples of curves, high dimensional data or discontinuous data. The techniques used include generalized linear models, estimating equations, change-point analysis, random effects modelling and smoothing (in particular, kernel and local polynomial smoothers).The proposed models with discontinuous parts are used to address the question whether the growth of children proceeds continuously or discontinuously. A second application is the modelling of DNA sequences here discontinuities in base frequencies enable DNA segmentation techniques. Further applications include edge detection in image analysis and the segmentation of continuously recorded financial data such as stock market indices. In addition, the P.I. constructs models for the efficient analysis of high dimensional data. An example is dose-response analysis in nutrition with the aim of finding optimal vitamin sources. The P.I. also proposes models for data where an entire function is recorded per experimental unit. Such data occur in many fields, notably the life sciences, and require innovative statistical approaches. A pertinent example are data on the individually recorded egg-laying behavior for a large cohort of medflies. A major question in aging research is the relation between reproduction and longevity. It is therefore of particular interest to apply the proposed models to explore this connection.

对于许多科学问题，非参数和半参数统计建模有助于更好地理解和分析。这包括曲线估计技术的应用。私家侦探探索结合了联合收割机平滑（非参数），非平滑（不连续）和参数组件的模型。对于高维数据，例如曲线数据和纵向数据，合理的建模方法包括实现降维的组件。这些通常是模型的参数部分，然后在第二步中将非参数平滑器应用于降维数据。 私家侦探开发面向科学问题的模型，这些问题以曲线样本，高维数据或不连续数据的形式产生数据。所使用的技术包括广义线性模型，估计方程，变点分析，随机效应建模和平滑（特别是，核和局部多项式平滑器）。所提出的模型与不连续的部分被用来解决的问题，儿童的增长是否连续或不连续。第二个应用是DNA序列的建模，这里碱基频率的不连续性使DNA分割技术成为可能。其他应用包括图像分析中的边缘检测和连续记录的金融数据（如股票市场指数）的分割。此外，PI。为高维数据的有效分析构建模型。一个例子是营养方面的剂量反应分析，目的是找到最佳的维生素来源。私家侦探还提出了数据模型，其中每个实验单元记录整个函数。这种数据出现在许多领域，特别是生命科学领域，需要创新的统计方法。一个相关的例子是一个大的地中海果蝇群体的单独记录的产卵行为的数据。老龄化研究的一个主要问题是生殖与寿命之间的关系。因此，它是特别感兴趣的应用所提出的模型来探索这种联系。