This book provides a self-contained, linear, and unified introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference forcomplex models and in understanding the properties of such methods. The targeted audience includes statisticians, biostatisticians, and other researchers with a background in mathematical statistics who have an interest in learning about and doing research in empirical processes and semiparametric inference but who would like to have a friendly and gradual introduction to the area. The book can be used either as a research reference or as a textbook. The level ofthe book is suitable for a second year graduate course in statistics or biostatistics, provided the students have had a year of graduate level mathematicalstatistics and a semester of probability. A self-contained, linear, and unified introduction to empirical processes and semiparametric inference. Homework problems are also included at the end of each chapter INDICE: Introduction. An Overview of The Empirical Processes. Overview of Semiparametric Inference. Case Studies I. Introduction to Empirical Processes.Preliminiaries 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. Introduction To Semiparametric Inference. Seimparametric Models and Efficiency. Efficient Inference for Fininte-Dimensional Parameters. Efficient Inference for Infinite-Dimensional Parameters. SemiparametricM-Estimators. Case Studies III.
- ISBN: 978-0-387-74977-8
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 476
- Fecha Publicación: 01/02/2008
- Nº Volúmenes: 1
- Idioma: Inglés