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

PSC In The News

RSS Feed icon

Shaefer and Edin's book ($2 a Day) cited in piece on political debate over plight of impoverished Americans

Eisenberg tracks factors affecting both mental health and athletic/academic performance among college athletes

Shapiro says Americans' low spending reflects "cruel lesson" about the dangers of debt

Highlights

Susan Murphy elected to the National Academy of Sciences

Maggie Levenstein named director of ISR's Inter-university Consortium for Political and Social Research

Arline Geronimus receives 2016 Harold R. Johnson Diversity Service Award

PSC spring 2016 newsletter: Kristin Seefeldt, Brady West, newly funded projects, ISR Runs for Bob, and more

Next Brown Bag

PSC Brown Bags
will resume fall 2016

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