JSM 2012 Home

JSM 2012 Online Program

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

Online Program Home

Abstract Details

Activity Number: 399
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #304565
Title: A Nonparametric Test of Missing Completely at Random for Incomplete Multivariate Data
Author(s): Yao Yu*+ and Jun Li
Companies: University of California at Riverside and University of California at Riverside
Address: 900 University Ave, Riverside, CA, 92507, United States
Keywords: missing data ; k-sample test ; nonparametric test
Abstract:

Missing values occur in many data sets in the real world, for example, data from clinical trials or survey. Knowing the type of missing mechanisms is critical for adopting appropriate statistical analysis procedure. Many statistical methods assume missing completely at random (MCAR) due to its simplicity. Therefore, it is important to test whether this assumption is satisfied before applying those procedures. In the literature, most of the procedures for testing MCAR were developed under normality assumption, which is sometimes difficult to justify in practice. We propose a nonparametric test of MCAR for incomplete multivariate data, which does not require distributional assumptions. The proposed test is proved to be consistent against all the alternative hypotheses. Simulation shows that the proposed procedure has the Type I error well controlled at the nominal level and also has good power against a variety of alternative hypotheses.


The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2012 program




2012 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.