An up-to-date comprehensive treatment of a classic text on missing data in statistics The
topic of missing data has gained considerable attention in recent decades. This new edition by
two acknowledged experts on the subject offers an up-to-date account of practical methodology
for handling missing data problems. Blending theory and application authors Roderick Little
and Donald Rubin review historical approaches to the subject and describe simple methods for
multivariate analysis with missing values. They then provide a coherent theory for analysis of
problems based on likelihoods derived from statistical models for the data and the missing data
mechanism and then they apply the theory to a wide range of important missing data problems.
Statistical Analysis with Missing Data Third Edition starts by introducing readers to the
subject and approaches toward solving it. It looks at the patterns and mechanisms that create
the missing data as well as a taxonomy of missing data. It then goes on to examine missing
data in experiments before discussing complete-case and available-case analysis including
weighting methods. The new edition expands its coverage to include recent work on topics such
as nonresponse in sample surveys causal inference diagnostic methods and sensitivity
analysis among a host of other topics. * An updated classic written by renowned authorities on
the subject * Features over 150 exercises (including many new ones) * Covers recent work on
important methods like multiple imputation robust alternatives to weighting and Bayesian
methods * Revises previous topics based on past student feedback and class experience *
Contains an updated and expanded bibliography The authors were awarded The Karl Pearson Prize
in 2017 by the International Statistical Institute for a research contribution that has had
profound influence on statistical theory methodology or applications. Their work has been no
less than defining and transforming. (ISI) Statistical Analysis with Missing Data Third
Edition is an ideal textbook for upper undergraduate and or beginning graduate level students
of the subject. It is also an excellent source of information for applied statisticians and
practitioners in government and industry.
