Nonparametric Statistical Methods For Complete and Censored Data - Tapa dura

Críticas

"[This book] has several strengths One is its level of presentation. Neither overly simplistic in explanation nor overly burdened with distracting proofs, the book accomplishes the rare feat of coherently representing some complex issues in nonparametric analysis. An additional strength is the ease with which the book can be used as a text-book for a graduate level nonparametric statistics class." - Journal of the Royal Statistical Society, Issue 167 (4) "[This] book will be useful for students in biostatistics, pharmaceutical statistics, business, psychology, and the social sciences. The examples discussed in each chapter help in understanding the concepts . The book is well written ." - Mathematical Reviews, Issue 2005d The coverage is quite complete and includes some methods not often see in other books. Desu and Raghavarao have compiled a solid set of basic methods for binomial/categorical response data and distribution-free methods. -Technometrics, Nov 2004

Reseña del editor

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.