From the reviews: "Introduction to Empirical Processes and Semiparametric Inference is a very good combination of both the empirical processes and semiparametric theories. This is the first book of its kind...I agree with the author that this book is 'more of a textbook than a research monograph.' As the semiparametric inference is currently an extremely active research area in statistical research, the book will open the door for graduate students to identify significant future research potentials. In fact, this book contains the author's newest research result, the application of semiparametric method in microarray data analysis. This book can be used as a textbook for graduate students in statistics, biostatistics, and economics (econometrics). In fact, the contents of this book can be tailored for different courses." "Generally, this is a great book on empirical processes and semiparametric methods. It should be on the must-read list for a serious statistician, biostatistician, or econometrician." (Biometrics, September 2008) "The main focus of this book is to introduce empirical processes and semiparametric inference methods to researchers interested in developing inferential tools for relatively complicated mathematical or statistical modeling problems. ...The material is structured in a sensible way supporting the learning and understanding of useful and challenging techniques of empirical processes and semiparametric inference. The book could well be very helpful for those studying and applying these techniques." (International Statistical Review 2008,77,2) "This book is an introduction to what is commonly called the modern theory of empirical processes - empirical processes indexed by classes of functions - and to semiparametric inference, and the interplay between both fields. ... This is clearly intended to be a book for the novice in empirical process theory and semiparametric inference. ... The main material is presented in a clearly arranged and logical order. ... will be useful to anybody who wants to learn about the modern theory of empirical processes and semiparametric inference." (Erich Hausler, Zentralblatt MATH, Vol. 1180, 2010)

• Overview.- An Overview of Empirical Processes.- Overview of Semiparametric Inference.- Case Studies I.- Empirical Processes.- to Empirical Processes.- Preliminaries for Empirical Processes.- Stochastic Convergence.- Empirical Process Methods.- Entropy Calculations.- Bootstrapping Empirical Processes.- Additional Empirical Process Results.- The Functional Delta Method.- Z-Estimators.- M-Estimators.- Case Studies II.- Semiparametric Inference.- to Semiparametric Inference.- Preliminaries for Semiparametric Inference.- Semiparametric Models and Efficiency.- Efficient Inference for Finite-Dimensional Parameters.- Efficient Inference for Infinite-Dimensional Parameters.- Semiparametric M-Estimation.- Case Studies III.

From the reviews: "Introduction to Empirical Processes and Semiparametric Inference is a very good combination of both the empirical processes and semiparametric theories. This is the first book of its kind...I agree with the author that this book is 'more of a textbook than a research monograph.' As the semiparametric inference is currently an extremely active research area in statistical research, the book will open the door for graduate students to identify significant future research potentials. In fact, this book contains the author's newest research result, the application of semiparametric method in microarray data analysis. This book can be used as a textbook for graduate students in statistics, biostatistics, and economics (econometrics). In fact, the contents of this book can be tailored for different courses." "Generally, this is a great book on empirical processes and semiparametric methods. It should be on the must-read list for a serious statistician, biostatistician, or econometrician." (Biometrics, September 2008) "The main focus of this book is to introduce empirical processes and semiparametric inference methods to researchers interested in developing inferential tools for relatively complicated mathematical or statistical modeling problems. ...The material is structured in a sensible way supporting the learning and understanding of useful and challenging techniques of empirical processes and semiparametric inference. The book could well be very helpful for those studying and applying these techniques." (International Statistical Review 2008,77,2) "This book is an introduction to what is commonly called the modern theory of empirical processes - empirical processes indexed by classes of functions - and to semiparametric inference, and the interplay between both fields. ... This is clearly intended to be a book for the novice in empirical process theory and semiparametric inference. ... The main material is presented in a clearly arranged and logical order. ... will be useful to anybody who wants to learn about the modern theory of empirical processes and semiparametric inference." (Erich Hausler, Zentralblatt MATH, Vol. 1180, 2010)

• Overview.- An Overview of Empirical Processes.- Overview of Semiparametric Inference.- Case Studies I.- Empirical Processes.- to Empirical Processes.- Preliminaries for Empirical Processes.- Stochastic Convergence.- Empirical Process Methods.- Entropy Calculations.- Bootstrapping Empirical Processes.- Additional Empirical Process Results.- The Functional Delta Method.- Z-Estimators.- M-Estimators.- Case Studies II.- Semiparametric Inference.- to Semiparametric Inference.- Preliminaries for Semiparametric Inference.- Semiparametric Models and Efficiency.- Efficient Inference for Finite-Dimensional Parameters.- Efficient Inference for Infinite-Dimensional Parameters.- Semiparametric M-Estimation.- Case Studies III.

From the reviews: "Introduction to Empirical Processes and Semiparametric Inference is a very good combination of both the empirical processes and semiparametric theories. This is the first book of its kind...I agree with the author that this book is 'more of a textbook than a research monograph.' As the semiparametric inference is currently an extremely active research area in statistical research, the book will open the door for graduate students to identify significant future research potentials. In fact, this book contains the author's newest research result, the application of semiparametric method in microarray data analysis. This book can be used as a textbook for graduate students in statistics, biostatistics, and economics (econometrics). In fact, the contents of this book can be tailored for different courses." "Generally, this is a great book on empirical processes and semiparametric methods. It should be on the must-read list for a serious statistician, biostatistician, or econometrician." (Biometrics, September 2008) "The main focus of this book is to introduce empirical processes and semiparametric inference methods to researchers interested in developing inferential tools for relatively complicated mathematical or statistical modeling problems. ...The material is structured in a sensible way supporting the learning and understanding of useful and challenging techniques of empirical processes and semiparametric inference. The book could well be very helpful for those studying and applying these techniques." (International Statistical Review 2008,77,2) "This book is an introduction to what is commonly called the modern theory of empirical processes - empirical processes indexed by classes of functions - and to semiparametric inference, and the interplay between both fields. ... This is clearly intended to be a book for the novice in empirical process theory and semiparametric inference. ... The main material is presented in a clearly arranged and logical order. ... will be useful to anybody who wants to learn about the modern theory of empirical processes and semiparametric inference." (Erich Hausler, Zentralblatt MATH, Vol. 1180, 2010)

• Overview.- An Overview of Empirical Processes.- Overview of Semiparametric Inference.- Case Studies I.- Empirical Processes.- to Empirical Processes.- Preliminaries for Empirical Processes.- Stochastic Convergence.- Empirical Process Methods.- Entropy Calculations.- Bootstrapping Empirical Processes.- Additional Empirical Process Results.- The Functional Delta Method.- Z-Estimators.- M-Estimators.- Case Studies II.- Semiparametric Inference.- to Semiparametric Inference.- Preliminaries for Semiparametric Inference.- Semiparametric Models and Efficiency.- Efficient Inference for Finite-Dimensional Parameters.- Efficient Inference for Infinite-Dimensional Parameters.- Semiparametric M-Estimation.- Case Studies III.