Balancing the "cookbook" approach of some texts with the more mathematical approach of others, Nonparametric Statistical Methods for Complete and Censored Data introduces commonly used non-parametric methods for complete data and extends those methods to right censored data analysis. Whenever possible, the authors derive their methodology from the general theory of statistical inference and introduce the concepts intuitively for students with minimal backgrounds. Derivations and mathematical details are relegated to appendices at the end of each chapter, which allows students to easily proceed through each chapter without becoming bogged down in a lot of mathematics. In addition to the nonparametric methods for analyzing complete and censored data, the book covers optimal linear rank statistics, clinical equivalence, analysis of block designs, and precedence tests. To make the material more accessible and practical, the authors use SAS programs to illustrate the various methods included. Exercises in each chapter, SAS code, and a clear, accessible presentation make this an outstanding text for a one-semester senior or graduate-level course in nonparametric statistics for students in a variety of disciplines, from statistics and biostatistics to business, psychology, and the social scientists. Prerequisites: Students will need a solid background in calculus and a two-semester course in mathematical statistics.

Balancing the "cookbook" approach of some texts with the more mathematical approach of others, Nonparametric Statistical Methods for Complete and Censored Data introduces commonly used non-parametric methods for complete data and extends those methods to right censored data analysis. Whenever possible, the authors derive their methodology from the general theory of statistical inference and introduce the concepts intuitively for students with minimal backgrounds. Derivations and mathematical details are relegated to appendices at the end of each chapter, which allows students to easily proceed through each chapter without becoming bogged down in a lot of mathematics.

In addition to the nonparametric methods for analyzing complete and censored data, the book covers optimal linear rank statistics, clinical equivalence, analysis of block designs, and precedence tests. To make the material more accessible and practical, the authors use SAS programs to illustrate the various methods included. These programs are provided both in the book and on the CRC Web site.

Exercises in each chapter, SAS code, and a clear, accessible presentation make this an outstanding text for a one-semester senior or graduate- level course in nonparametric statistics for students in a variety of disciplines, from statistics and biostatistics to business, psychology, and the social scientists.

Prerequisites: Students will need a solid background in calculus and a two-semester course in mathematical statistics.

An introduction to state-of-the-art experimental design approaches to better understand and interpret repeated measurement data in cross-over designs. Repeated Measurements and Cross-Over Designs: * Features the close tie between the design, analysis, and presentation of results * Presents principles and rules that apply very generally to most areas of research, such as clinical trials, agricultural investigations, industrial procedures, quality control procedures, and epidemiological studies * Includes many practical examples, such as PK/PD studies in the pharmaceutical industry, k-sample and one sample repeated measurement designs for psychological studies, and residual effects of different treatments in controlling conditions such as asthma, blood pressure, and diabetes. * Utilizes SAS(R) software to draw necessary inferences. All SAS output and data sets are available via the book's related website. This book is ideal for a broad audience including statisticians in pre-clinical research, researchers in psychology, sociology, politics, marketing, and engineering.