Variance Estimation after Mass Imputation Based on Combined Administrative and Survey Data

A unified approach to linearization variance estimation from survey data after imputation for item nonresponse

Variance estimation after imputation is an important practical problem in survey sampling. When deterministic imputation or stochastic imputation is used, we show that the variance of the imputed estimator can be consistently estimated by a unifying linearize and reverse approach. We provide some applications of the approach to regression imputation, fractional categorical imputation, multiple ...

Variance Estimation in the Presence of Imputation for Missing Data

Item nonresponse is usually treated by some form of deterministic or random imputation. We focus on deterministic imputation; in particular, ratio and nearest neighbour imputations commonly used in establishment surveys. Frequentist inference from imputed data is based on a repeated sampling framework and assumed response mechanism. On the other hand, use of imputation models requires only that...

Reporting Response Rates when Survey and Administrative Data are Combined

At Statistics Canada, annual and monthly business surveys are using administrative data at an ever increasing rate. An important set of administrative data is received from the Canada Revenue Agency as a result of its collection of income tax reports and the Goods and Services Tax reports. These data are not only used to build and maintain a central frame, such as Statistics Canada’s Business R...

Using the Fractional Imputation Methodology to Evaluate Variance due to hot Deck Imputation in Survey Data

An Empirical Comparison of Performance of the Unified Approach to Linearization of Variance Estimation after Imputation with Some Other Methods

Imputation is one of the most common methods to reduce item non_response effects. Imputation results in a complete data set, and then it is possible to use naϊve estimators. After using most of common imputation methods, mean and total (imputation estimators) are still unbiased. However their variances (imputation variances) are underestimated by naϊve variance estimators. Sampling mechanism an...