By J.L. Schafer
Offers a unified, Bayesian method of the research of incomplete multivariate info, overlaying datasets within which the variables are non-stop, express or either. contains genuine facts examples and sensible suggestion.
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Extra info for Analysis of Incomplete Multivariate Data
Medical screening with multiple tests, where not all tests are administered to all subjects. In many medical studies, an inexpensive or easily administered test is given to a large number of subjects, and for purposes of calibration a second, more expensive, and more reliable test is administered to a subsample. The calibrating sample may be chosen completely at random, on the basis of subject specific covariates, or even on the basis of the outcome of the first test. As long as all the information used to choose the subsample is recorded and regarded as part of the observed data Yobs, then the missing data will be MAR.
Another plausible estimate is the n complete-case (CC) estimate µ˜ 2 = n1−1Σ i =1 1 yi 2 , the average of Y2 among the completely observed cases. Estimating µ2 by µ˜ 2 may certainly be regarded as an ignorable procedure because it is consistent with the belief that the missing data are MCAR, a special case of MAR. , n. Consequently, the ML estimate tends to be more efficient than the CC estimate, and it exhibits better performance over a variety of missingness mechanisms. We can easily compare the performance of the ML and CC estimates by simulation.
T+1) (t) Example 2: Two binary variables with missing data on both. Suppose that Y1 and Y 2 are two potentially related dichotomous variables, each taking values 1 or 2. If the n units in a sample are iid, the complete data may, without loss of information, be reduced to an array of counts x=(x 11, x12, x21, x22) having a multinomial distribution, where xij is the number of sample units having Y1 = i and Y2 = j. Let θ = (θ11, θ12,, θ21, θ22), where θij is the probability that a unit has Y1 = i and Y2 = j.
Analysis of Incomplete Multivariate Data by J.L. Schafer