Statistical Analysis With Missing Data
Acknowledged experts on the subject bring together diverse sources on methods for statistical analysis of data sets with missing values, a pervasive problem for which standard methods are of limited value. Blending theory and application, it reviews historical approaches to the subject, and rigorous yet simple methods for multivariate analysis with missing values. Goes on to provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing data mechanism. The theory is applied to a wide range of important missing-data problems. Extensive references, examples, and exercises.
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Missing Data in Experiments
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adjustment cells algorithm analysis ANOVA applied assume assumption Bayesian bivariate normal block calculated categorical variables cell probabilities censoring Chapter CN CN complete-data completely observed component computations conditional distribution contingency table convergence correlation covariance matrix data are MAR defined degrees of freedom denote discussed equation Example exponential family factor function given Hence hot deck incomplete data inferences interval iteration least squares estimates linear regression loglikelihood loglinear model marginal distribution maximizing Maximum likelihood estimation MCAR methods missing data missing values missing-data mechanism ML estimates monotone pattern multiple imputation multivariate normal multivariate normal distribution nonignorable nonresponse normal data normal distribution normal with mean observed values obtained partially classified pattern of missing population mean posterior distribution problem procedure regression coefficients responding units Rubin sample mean Section simple random sampling standard errors step sufficient statistics sum of squares Suppose theory vector yields Ymis Yobs zero