Robust Rank-Based and Nonparametric Methods

The contributors to this volume include many of the distinguished researchers in this area. Many of these scholars have collaboratedwith Joseph McKean to develop underlying theory for these methods,obtain small sample corrections, and develop efficient algorithmsfor their computation.The papers cover the scope of the area, includingrobust nonparametric rank-based procedures through Bayesian and big datarank-based analyses.Areas of application include biostatistics andspatial areas. Over the last 30 years, robust rank-based and nonparametric methods have developed considerably.These procedures generalize traditional Wilcoxon-type methods for one- andtwo-sample location problems.Research into these procedures has culminated in complete analyses for manyof the models used in practice including linear, generalized linear, mixed,and nonlinear models. Settings are both multivariate and univariate.With the development of R packages in these areas, computation of these procedures is easilyshared with readers and implemented. This book is developed from the International Conference on Robust Rank-Based and Nonparametric Methods, held at Western Michigan University in April 2015.