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…
"Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple ana…
For more than two decades, the First Edition of Linear Regression Analysis has been an authoritative resource for one of the most common methods of handling statistical data. There have been many advances in the field over the last twenty years, including the development of more…
A first course in linear model theory
by Ravishanker, Nalini, Dey, Dipak
fromTexts in statistical science
This innovative, intermediate-level statistics text fills an important gap by presenting the theory of linear statistical models at a level appropriate for senior undergraduate or first-year graduate students. With an innovative approach, the author's introduces students to the …