This book provides a selfcontained linear and unified introduction toempirical processes and semiparametric inference. These powerful researchtechniques are sur... » 

This book provides a selfcontained linear and unified introduction toempirical processes and semiparametric inference. These powerful researchtechniques are surprisingly useful for developing methods of statisticalinference for complex models and in understanding the properties of suchmethods. The targeted audience includes statisticians biostatisticians andother researchers with a background in mathematical statistics who have aninterest in learning about and doing research in empirical processes andsemiparametric inference but who would like to have a friendly and gradualintroduction to the area. The book can be used either as a research referenceor as a textbook. The level of the book is suitable for a second year graduatecourse in statistics or biostatistics provided the students have had a year ofgraduate level mathematical statistics and a semester of probability. The bookconsists of three parts. The first part is a concise overview of all of themain concepts covered in the book with a minimum of technicalities. The secondand third parts cover the two respective main topics of empirical processes andsemiparametric inference in depth. The connections between these two topics isalso demonstrated and emphasized throughout the text. Each part has a finalchapter with several case studies that use concrete examples to illustrate theconcepts developed so far. The last two parts also each include a chapter whichcovers the needed mathematical preliminaries. Each main idea is introduced witha nontechnical motivation and examples are given throughout to illustrateimportant concepts. Homework problems are also included at the end of eachchapter to help the reader gain additional insights. «