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- W40887592 abstract "In the last decade, substantial progress has been made on methods for imputation of missing data. Modern imputation methods have become widely available for practitioners through software products such as S-Plus 6.0 (Schimert, Schafer, Hesterberg, Fraley, and Clarkson 2000), SAS PROC MI (SAS 2001), and SOLAS (2001) . The key idea underlying most of these methods is to impute missing values by random draws from the conditional distribution of the missing data given the observed data. In practice, many of these methods (e.g. the “norm” module of the “missing” library in S-Plus 6, SAS PROC MI) impose a multivariate normal distribution on the incompletely observed variables. When these variables are not normally distributed but rather categorical (binary or ordinal), practitioners are often advised to round the imputed value, typically drawn from a continuous multivariate normal, to the nearest integer (or category) that is within defined region. In this paper we provide some practical suggestions for rounding with binomial or ordinal variables that will enable practitioners to use commonly available software while providing imputed datasets that will lead to inferences that are less biased, in the sense that marginal distributions are more accurate." @default.
- W40887592 created "2016-06-24" @default.
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- W40887592 date "2002-01-01" @default.
- W40887592 modified "2023-09-27" @default.
- W40887592 title "Practical suggestions on rounding in multiple imputation" @default.
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