There are many competing computational algorithms in multiple imputation. To this date, however, it is unknown which of these algorithms outperforms the others under what circumstances. In this paper, we describe the mechanisms of various multiple imputation algorithms and compare their performance in a variety of situations to determine which algorithm is best suited to the imputation of missi...

Commonly in survey research, multiple, different analyses are conducted by one or more than one researcher on the same data set. The conclusions from these analyses should be consistent despite the presence of missing data. Multiple imputation is frequently used to ensure consistency of analyses. Two methods for multiple imputation of missing data are a combination of hot deck and regression im...

Imputation has enabled genomic selection in commercial livestock, taking advantage of a more cost effective Low Density (LD) panel, increasing the number of genotyped animals and hence accelerating the adoption process. A 5K LD panel has been employed commercially in New Zealand. This study investigated the accuracy of imputation to 50K and High Density (HD) panels using a new 15K panelbeing de...

Opinions expressed in this paper are the responsibility of the author alone and do not necessarily reflect the views of the Board of Governors or the Federal Reserve System. the advice and support of Fritz Scheuren, comments by Roderick Little and Donald Rubin on an earlier description of the imputation model presented here, the patience and creativity of Louise Woodburn in serving as sounding ...

Donald Rubin has suggested many times that one might multiply impute all the data in a survey as means of avoiding disclosure problems in public-use datasets. Disclosure protection in the Survey of Consumer Finances is a key issue driven by two forces. First, there are legal requirements stemming from the use of tax data in the sample design. Second, there is an ethical responsibility to protec...

This paper investigates the effect of parents’ current income and long-term family characteristics on individuals’ highest educational qualification obtained by age 26 using UK data from the 1970 British Cohort Study. The issues of the possible sample selection bias produced by the not completely random omission of current family income and that of its potential endogeneity are addressed, using...

This article discusses some properties of the rst order regression method for imputation of missing values on an explanatory variable in linear regression model and presents an estimation strategy based on hypothesis testing.

This paper provides extensions to the theory and the computational aspects of the Fellegi-Holt Model of Editing (JASA 1976). If implicit edits can be generated prior to editing, then error localization (finding the minimum number of fields to impute) can be quite rapid. In some situations, not all of the implicit edits can be generated because of the great number (> 10^30) of distinct edit patt...

INTRODUCTION For estimation of population characteristics (mainly totals, means, counts) in business statistics surveys, the Czech Statistical Office (CZSO) has been recently exploring a new approach, in which all data for units that are out of the sample are imputed based on predictions by regression, instead of estimating the population characteristics through weighting. The all-data imputati...

Missing data are a common occurrence in survey-based research studies in education, and the way missing values are handled can significantly affect the results of analyses based on such data. Despite known problems with performance of some missing data handling methods, such as mean imputation, many researchers in education continue to use those methods as a quick fix. This study reviews the cu...