Incorporating a large body of new work in the field, this second edition includes the latest applications of modern missing-data methods to real data. The authors also examine the theoretical and technical extensions that take advantage of recent computational advances.
Praise for the First Edition of Statistical Analysis with Missing Data ""An important contribution to the applied statistics literature ... I give the book high marks for unifying and making accessible much of the past and current work in this important area.""--William E. Straw…
The most user-friendly and authoritative resource on missing data has been completely revised to make room for the latest developments that make handling missing data more effective. The second edition includes new methods based on factored regressions, newer model-based imputat…
Statistical methods for handling incomplete data
In the era of big data, it is easy to imagine that we have all the information we need to make good decisions. But in fact the data we have are never complete, and may be only the tip of the iceberg. Just as much of the universe is composed of dark matter, invisible to us but no…
"Sooner or later anyone who does statistical analysis runs into problems with missing data in which information for some variables is missing for some cases. Why is this a problem? Because most statistical methods presume that every case has information on all the variables to b…
