Imputation and Variance Estimation for Survey Data

[803112] Jun shaoshaojun shaoshaojun Jun shaoshaojun Jun shaoshaojun Jun shaoshaojun Jun shaoshaojun Jun shaoshaojun研究者的重点是(1)验证和比较（理论上和经验上）现有的归算技术，并在必要时开发更好的程序；(2)为给定的估算方法开发正确的方差估计，从而产生正确的调查估计。我们将特别关注随机热甲板归算模型、最近邻归算、冷甲板归算、多变量归算、纵向归算、分位数归算和不可忽略响应归算。许多方差估计技术（如线性化/泰勒展开、折刀、平衡半样本或平衡重复复制、随机组和自举）将被研究。方差估计中将要解决的特殊问题包括不可忽略的抽样分数、应用复制方法（如分组和崩溃）的近似值、复杂和复合的imputation方法（在某种意义上，使用许多不同的imputation方法和/或使用输入的数据来对其他变量的非应答者进行imputation）、最近邻imputation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输入值的问题。大多数调查都没有受访者。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。通常用于处理项目无应答的补偿程序是为无应答者插入值的代入技术。通常的做法是将输入值视为已观察到的值，并使用为无响应情况设计的标准公式计算调查估计值并评估其可变性。然而，这可能会导致统计分析中的一些问题和偏差。例如，使用标准公式来评估分析中的变异性可能会严重低估真正的变异性，因为标准公式没有考虑由于无响应和/或归因而导致的变异性变化。本研究涉及发展正确和易于实施的统计程序，以分析调查数据与非受访者和imputation；并将解决人口普查局、劳工统计局和国家统计局等调查机构的一些实际统计问题。

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.