Collaborative Research: Theory and Methods for Nonparametric Survey Regression Estimation

合作研究：非参数调查回归估计的理论与方法

• 批准号: 0204531
• 负责人: Jay Breidt
• 金额: $ 7.12万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-15 至 2005-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0204531&HistoricalAwards=false
• 关键词: Collaborative; Research; Theory; Methods; Nonparametric

AbstractDMS-0204642 & DMS-0204531 (Collaborative)PIs: Opsomer/BriedtTitle: Collaborative Research: Theory and Methods for Nonparametric Survey Regression EstimationThis research project develops new methods for the efficient use of auxiliary information in complex surveys, based on nonparametric regression techniques. Current practice relies on parametric regression techniques, which have good efficiency if the regression model is well specified, and which have a number of appealing operational features. The nonparametric techniques share these operational features, lose little efficiency when the parametric specification is correct, and gain efficiency when the parametric specification is incorrect. The project increases the scope of applicability of the nonparametric regression estimation approach, by considering complex survey designs, varying types of auxiliary information, and alternative smoothing techniques. Specifically, the project investigates multi-stage surveys with cluster or element-level auxiliary information; multivariate auxiliary information; and alternative smoothing techniques. Parametric and nonparametric techniques are blended using semiparametric additive models to provide a flexible tool for use in complex surveys. Large-scale surveys are used to collect data in a wide range of fields, from studies of human populations to inventories of natural resources. Information external to the survey, such as administrative records or remote sensing, is often available. This research project makes it possible to incorporate auxiliary information easily and effectively into survey estimates, by using nonparametric regression methods. Nonparametric regression, sometimes referred to as smoothing, is widely used in other areas of statistics, but its use in survey estimation has been limited so far. The investigators show that incorporating auxiliary information into survey estimation through nonparametric regression can improve the precision of the surveys, often at reduced costs.

摘要DMS-0204642&Amp；DMS-0204531(协作式)PI：OpSomer/Briedt标题：协作性研究：非参数调查回归估计的理论和方法本研究项目基于非参数回归技术，开发了在复杂调查中有效利用辅助信息的新方法。目前的做法依赖于参数回归技术，如果回归模型被很好地指定，参数回归技术具有良好的效率，并且具有许多吸引人的操作特征。非参数技术分享了这些操作特征，当参数规范正确时，效率损失很小，而当参数规范不正确时，非参数技术获得效率。该项目通过考虑复杂的调查设计、不同类型的辅助信息和替代的平滑技术，扩大了非参数回归估计方法的适用范围。具体地说，该项目利用群组或要素一级的辅助信息、多变量辅助信息和替代平滑技术调查多阶段调查。参数技术和非参数技术使用半参数相加模型相结合，为复杂调查提供了一种灵活的工具。大规模调查被用来收集从人口研究到自然资源清单等广泛领域的数据。调查之外的信息，如行政记录或遥感，往往是可用的。这一研究项目使得使用非参数回归方法将辅助信息轻松有效地纳入调查估计成为可能。非参数回归，有时被称为平滑，在其他统计领域被广泛使用，但到目前为止，它在调查估计中的应用有限。研究人员表明，通过非参数回归将辅助信息纳入调查估计可以提高调查的精度，通常是在降低成本的情况下。