Fractional Imputation for Incomplete Data Analysis

不完整数据分析的分数插补

• 批准号: 1324922
• 负责人: Jae-Kwang Kim
• 金额: $ 25万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1324922&HistoricalAwards=false
• 关键词: Fractional; Imputation; Incomplete; Data; Analysis

Incomplete data frequently are encountered in survey data due to nonresponse, inaccurate measurement, and two-phase sampling, among other things. Any form of incomplete data can damage the representativeness of a sample, and a naive analysis with incomplete data can lead to biased estimation. Imputation is a process of assigning values to the missing items with the objective of reducing bias and improving the efficiency of the resulting estimators. This project will develop fractional imputation methods as a tool for handling incomplete data for general-purpose estimation. These methods will serve as important building blocks for the establishment of a complete statistical package for analysis of incomplete data that ultimately can be applied to problems in a variety of disciplines, including the social, behavioral, and economic sciences. In particular, the project will develop fractional imputation to address several important problems with incomplete data, including (1) likelihood-based inference, (2) robust estimation using fractional hot deck imputation, (3) synthetic imputation for survey integration, and (4) statistical matching technique. Because fractional imputation is a relatively new approach for handling incomplete data, there is a critical need for theoretical and methodological development. The advantages of the fractional imputation approach lie in its computational simplicity, wide applicability, and its statistical validity. By using fractional weights, fractional imputation avoids the burden of iterative computation, such as Markov Chain Monte Carlo, for the evaluation of conditional expectation associated with missing data. The proposed approach can be used to estimate parameters consistently and efficiently. The fractional imputation approach can be applied to nonstandard situations such as measurement error models, regression analysis combining two different surveys, and causal inference from observational studies. The impact of the proposed research is therefore substantial because the proposed approach can be used as a general methodology for incomplete data. Because of the computational simplicity and statistical validity of the fractional imputation approach, the results of the proposed research should have wide applicability. It also should have a major impact in providing complete data sets for analysis and new data products combining information from different surveys.

由于无响应，测量不正确和两相抽样，在调查数据中经常遇到不完整的数据。 任何形式的不完整数据都会损害样本的代表性，而具有不完整数据的天真分析可能会导致估计。 插补是将值分配给缺失项目的过程，目的是减少偏差并提高所得估计器的效率。 该项目将开发分数插补方法作为处理不完整数据以进行通用估计的工具。 这些方法将作为建立完整统计包的重要组成部分，以分析不完整的数据，最终可以应用于包括社会，行为和经济科学在内的各种学科中的问题。 特别是，该项目将开发分数插补，以解决不完整数据的几个重要问题，包括（1）基于可能性的推理，（2）使用分数热甲板插补的稳健估计，（3）用于调查集成的合成插补，以及（4）统计匹配技术。由于分数插补是处理不完整数据的一种相对较新的方法，因此对理论和方法论发展迫切需要。 分数插补方法的优势在于其计算简单性，广泛的适用性及其统计有效性。 通过使用分数权重，分数插补避免了迭代计算的负担，例如马尔可夫链蒙特卡洛（Monte Carlo），以评估与丢失数据相关的条件期望。 所提出的方法可用于始终如一地估计参数。 分数插补方法可以应用于非标准情况，例如测量误差模型，结合两种不同的调查的回归分析以及观察性研究的因果推断。 因此，拟议研究的影响很大，因为拟议的方法可以用作不完整数据的一般方法。 由于分数插补方法的计算简单性和统计有效性，拟议研究的结果应具有广泛的适用性。 它在提供分析的完整数据集和结合来自不同调查的信息的新数据集方面也应该产生重大影响。