Semiparametric Theory and Missing Data (Springer Series in Statistics) 2006th Edition by Anastasios Tsiatis (PDF)

Description

This book summarizes current knowledge regarding the theory of estimation for semiparametric models with missing data, in an organized and comprehensive manner. It starts with the study of semiparametric methods when there are no missing data. The description of the theory of estimation for semiparametric models is both rigorous and intuitive, relying on geometric ideas to reinforce the intuition and understanding of the theory. These methods are then applied to problems with missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐If you want to understand semiparametric theory and missing data, then read this book.

⭐lots of intuition.

⭐Tsiatis is a top researcher in biostatistics amd a good writer. Missing data is a very important issue in clinical trials and many models have been devised to handle them including multiple imputation (Rubin), pattern mixture models (Little) and mixed effects linear and nonlinear models (Pinheiro and Bates, Molenberghs and Verbeke and Davidian). Tsiatis takes a new approach with semiparametric models. I myself once looked at influence functions in the context of measuring imputation techniques that don’t influence estimates of importance. This approach is similar.

⭐A very well written book!

⭐This book would serve as a nice complement for (BKRW)’s book on efficient/adaptive semi parametric estimation. Theoretically less rigorous, but provides the much needed intuition about efficient estimation for semi parametric models and also goes on to cover missing data case. Also, the author’s writing style should be appreciated. At times there can be lot of crap in the name of explaining equations in English, but this book is clearly written and to the point.

⭐Most of the information to be found in this topic area comes from paper’s in scientific and statistical journals. This book brings all that information together into one place in a concise manner. The focus of the book is the theory of influence functions and while the theory is very difficult in places, the overall application to missing data puts most of that theory into a working context.

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