caa a22 4500 606241930 CHVBK 20210128101315.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s11336-014-9410-4 doi (NATIONALLICENCE)springer-10.1007/s11336-014-9410-4 A Nonparametric Test of Missing Completely at Random for Incomplete Multivariate Data [Elektronische Daten] [Jun Li, Yao Yu] Missing data occur in many real world studies. Knowing the type of missing mechanisms is important for adopting appropriate statistical analysis procedure. Many statistical methods assume missing completely at random (MCAR) due to its simplicity. Therefore, it is necessary 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. In this paper, we propose a nonparametric test of MCAR for incomplete multivariate data which does not require distributional assumptions. The proposed test is carried out by comparing the distributions of the observed data across different missing-pattern groups. We prove that the proposed test is consistent against any distributional differences in the observed data. Simulation shows that the proposed procedure has the Type I error well controlled at the nominal level for testing MCAR and also has good power against a variety of non-MCAR alternatives. The Psychometric Society, 2014 missing data nationallicence missing completely at random nationallicence nonparametric test nationallicence Li Jun Department of Statistics, University of California, Riverside, 92521, Riverside, CA, USA aut Yu Yao Department of Statistics, University of California, Riverside, 92521, Riverside, CA, USA aut Psychometrika Springer US; http://www.springer-ny.com 80/3(2015-09-01), 707-726 0033-3123 80:3<707 2015 80 11336 https://doi.org/10.1007/s11336-014-9410-4 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11336-014-9410-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Li Jun Department of Statistics, University of California, Riverside, 92521, Riverside, CA, USA aut NATIONALLICENCE 700 1- Yu Yao Department of Statistics, University of California, Riverside, 92521, Riverside, CA, USA aut NATIONALLICENCE 773 0- Psychometrika Springer US; http://www.springer-ny.com 80/3(2015-09-01), 707-726 0033-3123 80:3<707 2015 80 11336