Description: Statistical Analysis with Missing Data Roderick J. A. Little and Donald B. Rubin 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. 1987 0 471-80254-9 275 pp. Multiple Imputation for Nonresponse in Surveys Donald B. Rubin Demonstrates how nonresponse in sample surveys and censuses can be handled by replacing each missing value with two or more multiple imputations. Clearly illustrates the advantages of modern computing in handling such surveys, and demonstrates the benefit of this statistical technique for researchers who must analyze them. Also presents the background for Bayesian and frequentist theory. Examples and exercises reinforce ideas, and the interplay of Bayesian and frequentist ideas presents a unified picture of modem statistics. 1987 0 471-08705-X 250 pp. Measurement Error Models Wayne A. Fuller The first complete treatment of the theory and applications for measurement error problems (measurement of errors in collected data). Recent results for nonlinear models and for models with unequal variances are presented, the estimation of true values for the fixed models and prediction of true values under the random model are discussed, and models checks and the analysis of residuals are treated. Includes real-data examples along with twenty different data sets. 1987 0 471-86187-1 450 pp.

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