By J.L. Schafer

Provides a unified, Bayesian method of the research of incomplete multivariate info, protecting datasets within which the variables are non-stop, express or either. comprises actual facts examples and sensible recommendation.

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Randomized experiments with unequal numbers of cases per treatment group. In many designed experiments, the researcher strives to assign an equal number of cases or subjects to each treatment, because data that are balanced in ©1997 CRC Press LLC this fashion are typically easier to analyze than data that are unbalanced. Moreover, principles of efficiency often support the use of balanced designs. Sometimes balance is not feasible, however, and the data are unbalanced by design. The analysis of unbalanced data can often be simplified by imagining a number of additional cases which, if they were included in the experiment under the appropriate treatment groups, would result in a balanced experiment.

Assumption (a) often does hold in practice; it would be somewhat unusual, for example, for stratification variables in a sample survey or treatment indicators in a planned experiment to be missing. 21) will not precisely hold, but it may still be approximately true provided that the amount of information missing on the predictors is not large. 1) show the weight gains of two groups of female rats, one fed a low-protein diet and the other fed a high-protein diet. The low-protein group has 7 rats and the high-protein group has 12.

If all the variables used in the sampling plan are included in Yobs, then the missing data will be MAR. In most of the above examples, the missing data may be said to be missing by design, because it was never the intention of the investigator to record all potential variables for all subjects. When missing data are missing by design, they tend also to be MAR. 2 Examples where ignorability is not known to hold In many other missing-data contexts, however, it is not known whether or not the MAR condition is satisfied.

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