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

PSC In The News

RSS Feed icon

Buchmueller says employee wages are hit harder than corporate profits by rising health insurance costs

Davis-Kean et al. link children's self-perceptions to their math and reading achievement

Yang and Mahajan examine how hurricanes impact migration to the US

More News

Highlights

Pamela Smock elected to PAA Committee on Publications

Viewing the eclipse from ISR-Thompson

Paula Fomby to succeed Jennifer Barber as Associate Director of PSC

PSC community celebrates Violet Elder's retirement from PSC

More Highlights

Next Brown Bag

Mon, Sept 11, 2017, noon:
Welcoming of Postdoctoral Fellows: Angela Bruns, Karra Greenberg, Sarah Seelye and Emily Treleaven

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