Robust Rank-Based and Nonparametric Methods

Michigan, USA, April 2015: Selected, Revised, and Extended Contributions

  • Regina Y. Liu
  • Joseph W. McKean
Conference proceedings

Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 168)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Joseph W. McKean, Thomas P. Hettmansperger
    Pages 1-24
  3. Asheber Abebe, Huybrechts F. Bindele
    Pages 25-45
  4. Asheber Abebe, Joseph W. McKean, John D. Kloke, Yusuf K. Bilgic
    Pages 61-79
  5. Arne C. Bathke, Solomon W. Harrar
    Pages 121-139
  6. Gib Bassett
    Pages 249-260
  7. Karen V. Rosales, Joshua D. Naranjo
    Pages 261-272
  8. Back Matter
    Pages 273-277

About these proceedings


The contributors to this volume include many of the distinguished researchers in this area. Many of these scholars have collaborated with Joseph McKean to develop underlying theory for these methods, obtain small sample corrections, and develop efficient algorithms for their computation. The papers cover the scope of the area, including robust nonparametric rank-based procedures through Bayesian and big data rank-based analyses. Areas of application include biostatistics and spatial areas. Over the last 30 years, robust rank-based and nonparametric methods have developed considerably. These procedures generalize traditional Wilcoxon-type methods for one- and two-sample location problems. Research into these procedures has culminated in complete analyses for many of 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 easily shared 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. 

  • Includes theoretical research, novel applications of the methods, and research in computational procedures for these methods
  • Topics span robust rank-based procedures for current models, like general linear models and cluster correlated models; robust rank-based multivariate methods, including affine invariant procedures; robust procedures for spatial analyses; and robust rank-based Bayesian procedures
  • Includes implementation in R packages where possible


Bayesian and big data rank-based analyses Cluster correlated models General linear models Nonparametric Statistics Robust procedures for spatial analyses Robust rank-based Bayesian statistics Robust rank-based multivariate methods

Editors and affiliations

  • Regina Y. Liu
    • 1
  • Joseph W. McKean
    • 2
  1. 1.Department of StatisticsRutgers UniversityNew BrunswickUSA
  2. 2.Department StatisticsWestern Michigan UniversityKalamazooUSA

Bibliographic information

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