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Analysis of Incomplete Multivariate Data by J.L. Schafer PDF

By J.L. Schafer

ISBN-10: 0412040611

ISBN-13: 9780412040610

Offers a unified, Bayesian method of the research of incomplete multivariate info, overlaying datasets during which the variables are non-stop, specific or either. contains genuine info examples and useful recommendation.

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Extra info for Analysis of Incomplete Multivariate Data

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2) where l(θ | Y ) = logP (Y|θ) denotes the complete-data loglikelihood, l(θ|Yo b s ) = log P ( Y|θ) the observed-data loglikelihood, and c an arbitrary constant. The term P(Ymis|Yobs , θ), which we shall call the predictive distribution of the missing data given θ, plays a central role in EM because it captures the interdependence between Ymis , and θ . When viewed as a probability distribution it summarizes knowledge about Ymis, for any assumed value of θ, and when viewed as a function of θ it conveys the evidence about θ contained in Ymis beyond that already provided by Yobs.

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. Snedecor and Cochran perform a classical analysis assuming that the observations are independent and normally distributed and the within-group variances are equal. 3. 24) where tv,p denotes the pth quantile of the t distribution with v degrees of freedom. 05 level. 3 as incomplete data from a balanced experiment; that is, we now suppose that the lowprotein group had 12 potential observations, 5 of which are missing.

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. Examples include: 1. Sample surveys where some sampled individuals are not at home, unwilling to be interviewed, or do not otherwise provide useful responses to some or all of the questionnaire items. Notice, however, that if followup data can later be obtained for a probability sample of nonrespondents, the missing data can be converted to MAR.

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Analysis of Incomplete Multivariate Data by J.L. Schafer


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