期刊
STATA JOURNAL
卷 13, 期 4, 页码 795-809出版社
STATA PRESS
DOI: 10.1177/1536867X1301300407
关键词
st0318; mcartest; CDM; MAR; MCAR; MNAR; chi-squared; missing data; missing-value patterns; multivariate; power
In missing-data analysis, Little's test (1988, Journal of the American Statistical Association 83: 1198-1202) is useful for testing the assumption of missing completely at random for multivariate, partially observed quantitative data. I introduce the mcartest command, which implements Little's missing completely at random test and its extension for testing the covariate-dependent missingness. The command also includes an option to perform the likelihood-ratio test with adjustment for unequal variances. I illustrate the use of mcartest through an example and evaluate the finite-sample performance of these tests in simulation studies.
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