Imputation Methodology for Complex Survey Problems

复杂调查问题的插补方法

• 批准号: 0102223
• 负责人: Jun Shao
• 金额: $ 9.79万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-08-15 至 2005-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0102223&HistoricalAwards=false
• 关键词: Imputation; Methodology; Complex; Survey; Problems

The proposed research focuses on imputation and variance estimation after imputation for survey data with nonresponse. Marginal imputation (such as random hot deck imputation, nearest neighbor imputation, and random regression imputation) will be studied for the purpose of estimating population totals and quantiles. The investigator will also study joint imputation (for estimating parameters such as the coefficients of correlation or the cell probabilities in a contingency table) and imputation under nonignorable response. For each imputation method, variance estimation that takes nonresponse and imputation into account will be studied, using a direct derivation approach or a replication method such as the jackknife, the balanced half samples, and the bootstrap.Many statistics and government agencies collect data through surveys. Most surveys have nonresponse. Item nonresponse occurs when some sampled units cooperate in the survey but fail to provide answers to some questions. Imputation techniques, which insert values for nonrespondents, are commonly used compensation procedures for item nonresponse. In some cases, when auxiliary information is properly used, imputation increases statistical accuracy. An essential requirement for an imputation method is that one can obtain unbiased (or approximately unbiased) survey estimators by treating the imputed values as observed data and using the standard estimation formulas designed for the case of no nonresponse. This requires developments on imputation methodology and statistical analysis procedures to take nonresponse and imputation into account. Since most of the proposed research topics are motivated by problems in survey agencies such as the Census Bureau, the Bureau of Labor Statistics, Westat, and Statistics Canada, results obtained from the proposed research will have significant impacts on the imputation and variance estimation methodology for these survey agencies.

本文主要研究无回答调查数据的插补和插补后的方差估计问题。 为了估计总体和分位数，将研究边缘插补（如随机热甲板插补、最近邻插补和随机回归插补）。 研究者还将研究联合插补（用于估计相关系数或列联表中的单元格概率等参数）和不可解释应答下的插补。 对于每种插补方法，将使用直接推导方法或重复方法（如刀切法、平衡半样本法和自助法）研究考虑无应答和插补的方差估计。许多统计和政府机构通过调查收集数据。 大多数调查没有回答。项目无应答是指某些抽样单位在调查中合作，但未能提供某些问题的答案。插补技术，插入值的nonresponses，是常用的补偿程序项目无应答。 在某些情况下，如果适当使用辅助信息，插补可提高统计准确性。 插补方法的一个基本要求是，通过将插补值视为观察数据并使用为无无应答情况设计的标准估计公式，可以获得无偏（或近似无偏）的调查估计量。 这就需要发展估算方法和统计分析程序，以考虑到无答复和估算。 由于大多数拟议的研究课题的动机调查机构，如人口普查局，劳工统计局，Westat和加拿大统计局的问题，从拟议的研究所获得的结果将有显着影响这些调查机构的插补和方差估计方法。