Using the norm, models are easily fitted. Upon satisfaction with the fit, rank-based inferential procedures can be used to conduct the statistical analysis. The benefits include significant gains in power and efficiency when the error distribution has tails heavier than those of a normal distribution and superior robustness properties in general. The main text concentrates on Wilcoxon and L-1 methods. By restricting attention to Wilcoxon rank methods, we can recommend a unified approach to data analysis beginning with the simple location models and extending through complex regression models and designed experiments.
All major methodology is illustrated on real data.
The examples are intended as guides for the application of the rank and L-1 methods. Selected topics from the first four chapters provide a basic graduate course in rank based methods. The prerequisites are an introductory course in mathematical statistics and some background in applied statistics. In Chapter 3, on the linear model, sections one through seven and section nine present the basic material for estimation, testing and diagnostic procedures for model criticism. Then, depending on individual tastes, there are several more exotic topics in each chapter to choose from.