Analysis of Survey Data Using Imputation for Nonrespondents

在处理调查中的无反应时，归因是一种流行的技术。该项目侧重于发展估算方法，当将估算值视为观测数据并应用标准方法计算调查估算值时，可以产生近似无偏和有效的调查估算值。本课程将研究各种不同的输入方法，如最近邻输入、核非参数回归输入、经验似然和处理测量误差的技术。重点将放在多变量调查变量和/或多变量协变量的研究，以及在主要调查变量和协变量中存在的无响应问题。对于每种归算方法，将研究考虑非响应和归算的方差估计，使用直接推导方法或包含重新归算成分的复制方法（如jackknife，平衡一半样本，随机组和bootstrap）来评估由归算引起的变异性。特别地，将研究一些减少计算量的快捷复制方法。许多统计机构和政府机构通过调查收集数据。大多数调查都没有回应。部分抽样单位在调查中予以配合，但未对某些问题作出回答，即为项目不答复。为无应答者插入值的Imputation技术是对项目无应答的常用补偿程序。在某些情况下，如果适当地使用辅助信息，则可增加统计的准确性。对一种估算方法的一个基本要求是，通过将估算值当作观测数据，并使用为无响应情况设计的标准估算公式，可以获得无偏（或近似无偏）的调查估计量及其变异性估计量。这就需要发展归责方法和统计分析程序，以考虑到无反应和归责。由于大部分的研究课题都是由人口普查局（Census Bureau）、劳工统计局（Bureau of Labor Statistics）、西部统计局（Westat）和加拿大统计局（Statistics Canada）等调查机构存在的问题所驱动的，因此研究结果将对这些调查机构的归算和方差估计方法产生重大影响。作为支持调查和统计方法研究的联合活动的一部分，该研究得到了方法论、测量和统计计划、统计和概率计划以及联邦统计机构联盟的支持。

Imputation is a popular technique in handling nonresponse in surveys. This project focuses on the development of imputation methods that produce approximately unbiased and efficient survey estimators when imputed values are treated as observed data and standard methods are applied to compute the survey estimators. Various imputation methods will be studied, such as the nearest neighbor imputation, kernel nonparametric regression imputation, empirical likelihood, and techniques of handling measurement error. Emphasis will be placed on the study of multivariate survey variables and/or multivariate covariates, and problems with nonresponse in not only the main survey variables but also the covariates. 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, the random groups, and the bootstrap) that contains a re-imputation component to assess the variability caused by imputation. In particular, some shortcut replication methods that reduce the amount of computation will be investigated.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 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. The research is supported by the Methodology, Measurement, and Statistics Program, the Statistics and Probability Program, and a consortium of federal statistical agencies as part of a joint activity to support research on survey and statistical methodology.