Conditioning in a Missing Data Problem.
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WISCONSIN UNIV-MADISON MATHEMATICS RESEARCH CENTER
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Observations are recorded on variables x and y but a mechanism, which may depend on the observed x values, causes some of the y values to be missing. For three parametric examples, exact or approximate ancillary statistics are constructed. Conditioning on these ancillaries enables the missing data mechanism to be ignored under certain conditions. A correspondence is shown between these conditional procedures and the use of the observed information matrix in measuring the dispersion of the maximum likelihood estimator. Keywords Affine ancillary Ancillary statistic Conditional inference Curved exponential family Ignorability Information Missing data Survey sampling.
- Statistics and Probability