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

PSC In The News

RSS Feed icon

Lam looks at population and development in next 15 years in UN commission keynote address

Mitchell et al. find harsh family environments may magnify disadvantage via impact on 'genetic architecture'

Frey says Arizona's political paradoxes explained in part by demography


PSC newsletter spring 2014 issue now available

Kusunoki wins faculty seed grant award from Institute for Research on Women and Gender

2014 PAA Annual Meeting, May 1-3, Boston

USN&WR ranks Michigan among best in nation for graduate education in sociology, public health, economics

Next Brown Bag

Monday, April 21
Grant Miller: Managerial Incentives in Public Service Delivery

On the Performance of Sequential Regression Multiple Imputation Methods with Non Normal Error Distributions

Publication Abstract

He, Y.L., and Trivellore Raghunathan. 2009. "On the Performance of Sequential Regression Multiple Imputation Methods with Non Normal Error Distributions." Communications in Statistics-Simulation and Computation, 38(4): 856-883.

Sequential regression multiple imputation has emerged as a popular approach for handling incomplete data with complex features. In this approach, imputations for each missing variable are produced based on a regression model using other variables as predictors in a cyclic manner. Normality assumption is frequently imposed for the error distributions in the conditional regression models for continuous variables, despite that it rarely holds in real scenarios. We use a simulation study to investigate the performance of several sequential regression imputation methods when the error distribution is flat or heavy tailed. The methods evaluated include the sequential normal imputation and its several extensions which adjust for non normal error terms. The results show that all methods perform well for estimating the marginal mean and proportion, as well as the regression coefficient when the error distribution is flat or moderately heavy tailed. When the error distribution is strongly heavy tailed, all methods retain their good performances for the mean and the adjusted methods have robust performances for the proportion; but all methods can have poor performances for the regression coefficient because they cannot accommodate the extreme values well. We caution against the mechanical use of sequential regression imputation without model checking and diagnostics.

DOI:10.1080/03610910802677191 (Full Text)

Browse | Search : All Pubs | Next