Imputation for Survey Data with Ignorable or Nonignorable Nonresponse

对具有可忽略或不可忽略的无答复的调查数据进行插补

• 批准号: 0404535
• 负责人: Jun Shao
• 金额: $ 11.1万
• 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: 2004
• 资助国家: 美国
• 起止时间: 2004-09-15 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0404535&HistoricalAwards=false
• 关键词: Imputation; Survey; Data; Ignorable; Nonignorable

The proposed research focuses on imputation and varianceestimation after imputation for survey data with nonresponse.The investigator will study different models that relate auxiliary variables and the variable to be imputed (e.g., parametric, non-parametric, and semi-parametric models); different response mechanisms(ignorable or non-ignorable); various imputation techniques (e.g., regression,nearest neighbor, and random imputation);different types of estimators (e.g., sample mean andsample quantiles); and different types of data(e.g., cross-sectional, clustered, or longitudinal data).The investigator will also study a pseudo empirical likelihoodimputation method that provides more efficient survey estimatorsthan other imputation methods.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)that contains a re-imputation component to assess the variabilitycaused by imputation. 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 and their variability estimators by treating the imputed values as observed data and using the standard estimation formulas designed for the case of no nonresponse. This requires developmentson imputation methodology and statistical analysis proceduresto 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.

拟议的研究重点是无应答调查数据的插补和插补后的方差估计。研究者将研究将辅助变量与待插补变量（例如，参数、非参数和半参数模型）;不同的响应机制（可验证的或不可验证的）;各种估算技术（例如，回归、最近邻和随机插补）;不同类型的估计量（例如，样本平均值和样本分位数）;以及不同类型的数据（例如，研究者还将研究一种伪经验似然插补方法，该方法提供了比其他插补方法更有效的调查估计值。对于每种插补方法，将使用直接推导方法或重复方法研究考虑无应答和插补的方差估计（如刀切法、平衡半样本法和自助法），其中包含重新插补成分，以评估插补引起的变异性。许多统计机构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响应情况设计的标准估计公式，可以获得无偏（或近似无偏）调查估计量及其变异性估计量。这需要发展插补方法和统计分析程序，以考虑无应答和插补。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。