Imputation and Variance Estimation for Survey Data

9803112邵军本研究涉及非被调查者调查数据的插补技术和方差估计方法的发展。 研究者的重点是（1）验证和比较（理论和经验）现有的插补技术，并在必要时开发更好的程序;（2）为给定的插补方法开发正确的方差估计量，以产生正确的调查估计。 将特别注意使用模型的随机热甲板插补、最近邻插补、冷甲板插补、多变量插补、纵向插补、分位数插补和不可验证响应的插补。 许多方差估计技术（如线性化/泰勒展开，刀切，平衡半样本或平衡重复复制，随机组和自助）将被研究。 在方差估计中将讨论的特殊问题包括不可忽略的抽样分数、应用重复方法时的近似值（如分组和折叠）、复杂和复合插补方法（在某种意义上，使用许多不同的插补方法和/或插补数据来插补其他变量的非应答者），最近邻插补的方差估计，样本分位数的方差估计，以及无法从数据集中识别的估算值的问题。大多数调查都有非受访者。 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。处理项目无应答的常用补偿程序是插补技术，它为无应答者插入值。 通常的做法是将插补值视为已观察到的值，并使用为无无答复情况设计的标准公式计算调查估计值并评估其变异性。 然而，这可能导致统计分析中的一些问题和偏差。 例如，使用标准公式评估分析中的变异性可能会严重低估真实的变异性，因为标准公式无法解释由于无应答和/或插补而导致的变异性变化。 这项研究涉及正确和简单的统计程序的发展，分析调查数据与nonresponses和插补，并将解决一些真实的统计调查机构，如人口普查局，劳工统计局，和Westat的问题。

9803112 Jun ShaoThis research involves development of imputation techniques and variance estimation methods for survey data with nonrespondents. The investigator focues on (1) validating and comparing (both theoretically and empirically) the existing imputation techniques and developing better procedures if necessary; and (2) developing correct variance estimators for a given imputation method that produces correct survey estimates. Special attention will be paid on random hot deck imputation using models, nearest neighbor imputation, cold deck imputation, multivariate imputation, longitudinal imputation, imputation for quantiles, and imputation for non-ignorable response. Many variance estimation techniques (such as the linearization/Taylor expansion, jackknife, balanced half sample or balanced repeated replication, random groups, and bootstrap) will be studied. Particular issues that will be addressed in variance estimation include non-negligible sampling fractions, approximation in applying replication methods (such as grouping and collapsing), complex and composite imputation methods (in the sense that a number of different imputation methods are used and/or imputed data are used to impute nonrespondents for other variables), variance estimation for nearest neighbor imputation, variance estimation for sample quantiles, and problems with imputed values that cannot be identified from the data set.Most surveys have nonrespondents. Item nonresponse occurs when some sampled units cooperate in the survey but fail to provide answers to some questions. Commonly used compensation procedures for handling item nonresponse are imputation techniques which insert values for nonrespondents. It is a common practice to treat the imputed values as if they had been observed, and compute survey estimates and assess their varibility using standard formulas designed for the case of no nonresponse. This, however, could lead to some problems and biases in statistical analysis. For example, the use of standard formulas to assess varibility in analysis may seriously underestimate the true varibility, because standard formulas do not account for the changes in varibility due to nonresponse and/or imputation. This research involves development of correct and simple to implement statistical procedures to analyze survey data with nonrespondents and imputation; and will solve some real statistical problems in survey agencies such as the Census Bureau, the Bureau of Labor Statistics, and Westat.