Little's test of missing completely at random
Abstract. 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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Cheng Li
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mcartest, CDM, MAR, MCAR, MNAR, chi-squared, missing data, missing-value patterns, multivariate, power
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