Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Mitchell finds children who lose fathers suffer at cellular level

Seefeldt says hard work alone won't allow poor to reach middle-class status in America

Shaefer says proposed plan to cover tax cuts would hurt a lot of struggling Americans

More News

Highlights

Neal Krause wins GSA's Robert Kleemeier Award

MiCDA Research Fellowship - applications due July 21, 2017

U-M awarded $58 million to develop ideas for preventing and treating health problems

Bailey, Eisenberg , and Fomby promoted at PSC

More Highlights

Multiple imputation using multivariate gh transformations

Publication Abstract

He, Y., and Trivellore Raghunathan. 2012. "Multiple imputation using multivariate gh transformations." Journal of Applied Statistics, 39(10): 2177-2198.

Multiple imputation has emerged as a popular approach to handling data sets with missing values. For incomplete continuous variables, imputations are usually produced using multivariate normal models. However, this approach might be problematic for variables with a strong non-normal shape, as it would generate imputations incoherent with actual distributions and thus lead to incorrect inferences. For non-normal data, we consider a multivariate extension of Tukey's gh distribution/transformation [38] to accommodate skewness and/or kurtosis and capture the correlation among the variables. We propose an algorithm to fit the incomplete data with the model and generate imputations. We apply the method to a national data set for hospital performance on several standard quality measures, which are highly skewed to the left and substantially correlated with each other. We use Monte Carlo studies to assess the performance of the proposed approach. We discuss possible generalizations and give some advices to practitioners on how to handle non-normal incomplete data.

DOI:10.1080/02664763.2012.702268 (Full Text)

Browse | Search : All Pubs | Next