Semiparametric Theory and Missing Data (Springer Series in Statistics)
From the reviews:"The author, who does not need an introduction…had presented with clarity how he views three different subjects within a unified approach for statistical inference.…It is a long awaited book for a large audience of graduate students and researchers who have often found this subject matter daunting.… It is an easy decision for me to recommend this book to anyone who is interested in learning and using theories of frequentist estimation for semiparametric models and coarsened data. Even beyond his/her graduate student days, any statistical researcher interested in mastering frequentist semiparamatric estimation can pick up all the essential information from this book." (Debajyoti Sinha, American Statistical Association, JASA, March 2009, Vol. 104, No. 485)"Since much of the work in this area is very technical, it is most welcome to have a self-contained clearly written account by a highly-regarded author. The application to missing data is also clearly of great interest." R.J.A. Little for Short Book Reviews of the ISI, December 2006"This book is focused precisely on the problem of estimation for a semiparametric model when the data are missing. This comprehensive monograph offers an in-depth look at the associated theory … . It was a great pleasure to read this masterful account of semiparametric theory for missing data problems … . It provides a valuable resource because it contains an up-to-date literature review and an exceptional account of state of the art research on the necessary theory. … I recommend it to any professional statistician." (Konstantinos Fokianos, Technometrics, Vol. 49 (2), 2007)"The book under review deals with estimation for SMs with missing, coarsened, and censored data. … The book is very clearly and informally written. The exposition is instructive and rigorous enough. There are many important examples, oriented to biomedical applications. The monograph will be useful for graduate and post-graduate students in statistics and biostatistics, as well as researchers in statistics and survival analysis." (Oleksandr Kukush, Zentralblatt MATH, Vol. 1105 (7), 2007)