Affiliation:
1. Associate Professor in the Ohio State University, 1841 Neil Ave, Columbus, OH 43210, USA
2. US Census Bureau, 4600 Silver Hill Rd, Washington, DC 20233, USA
Abstract
Abstract
Detailed breakdowns on totals are often collected in surveys, such as a breakdown of total product sales by product type. These multinomial data are often sparsely reported with wide variability in proportions across units. In addition, there are often true zeros that differ across units even within industry; for example, one establishment sells jeans but not shoes, and another sells shoes but not socks. It is quite common to have large fractions of missing data for these detailed items, even when totals are relatively completely observed. Hot-deck imputation, which fills in missing data with observed data values, is an attractive approach. The entire set of proportions can be simultaneously imputed to preserve multinomial distributions, and zero values can be imputed. However, it is not clear what variant of the hot deck is best. We describe a large set of “flavors” of the hot deck and compare them through simulation and by application to data from the 2012 Economic Census. We consider different ways to create the donor pool: choosing one nearest neighbor (NN), choosing from five NNs, or using all units as the donor pool. We also consider different ways to impute from the donor: directly impute the donor’s vector of proportions or randomly draw from a multinomial distribution using this vector of proportions. We consider scenarios where a strong predictor of these multinomial distributions exists as well as when covariate information is weak.
Publisher
Oxford University Press (OUP)
Subject
Applied Mathematics,Statistics, Probability and Uncertainty,Social Sciences (miscellaneous),Statistics and Probability
Reference14 articles.
1. The Use of Sample Weights in Hot Deck Imputation;Andridge;Journal of Official Statistics,2009
2. A Review of Hot Deck Imputation for Survey Non-Response;Andridge;International Statistical Review,2010
3. Variance Estimation When Donor Imputation Is Used to Fill in Missing Values;Beaumont;Canadian Journal of Statistics,2009
4. How to Obtain Valid Inference under Unit Nonresponse;Boeschoten;Journal of Official Statistics,2017
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