Editors:
- 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
- Includes supplementary material: sn.pub/extras
Part of the book series: Springer Proceedings in Mathematics & Statistics (PROMS, volume 168)
Buy it now
Buying options
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (15 papers)
-
Front Matter
-
Back Matter
About this book
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.
Editors and Affiliations
-
Department of Statistics, Rutgers University, New Brunswick, USA
Regina Y. Liu
-
Department Statistics, Western Michigan University, Kalamazoo, USA
Joseph W. McKean
About the editors
Dr. Joseph McKean is Professor of Statistics at Western Michigan University. He received hisPhD in Statistics in 1975 from the Pennsylvania State University under the direction of Professor T.P. Hettmansperger. He has held several visiting research professorships at University of New South Wales. In 1999, he was elected as a fellow of the American Statistical Association. In 1994, he received the Distinguished Faculty Scholar Award from Western Michigan University. He served as Chair of the Nonparametric Section of the American Statistical Association during 2002. Dr. McKean has served on the editorial board of several statistical journals, including the Journal of the American Statistical Association, the Journal of Statistical Computation and Simulation, and the Journal of Nonparametric Statistics.
Dr. McKean has published extensively on robust rank-based procedures for linear models. These include papers on the theory for robust estimation and testing, the geometry of robust procedures, and the small sample properties of robust inference. He has worked with general robust estimates, bounded inuence estimates, and high breakdown estimates. He has co-authored a series of papers on diagnostic procedures for robust estimation. Besides robust procedures, Dr. McKean has published in the areas of generalized linear models, nonparametric statistics and time series analyses. He has recently published articles on rank-based procedures for nonlinear, mixed, and GEE models. He is a co-author (with T.P. Hettmansperger) of the monograph Robust Nonparametric Statistical Methods. He has worked on algorithm development and software for these procedures including the R package Rfit and has co-authored (with J.D. Kloke) the book Nonparametric Statistical Methods Using R. His current investigations include rank-based algorithms for Big Data, rank-based Bayesian methods for linear and mixed models, visualization techniques, and robust methods for linear models with autoregressive errors. Dr. McKean has served as the dissertation advisor for twenty-six PhD students. He is a co-author, (with R.V. Hogg), of the text, Introduction to Mathematical Statistics.
Bibliographic Information
Book Title: Robust Rank-Based and Nonparametric Methods
Book Subtitle: Michigan, USA, April 2015: Selected, Revised, and Extended Contributions
Editors: Regina Y. Liu, Joseph W. McKean
Series Title: Springer Proceedings in Mathematics & Statistics
DOI: https://doi.org/10.1007/978-3-319-39065-9
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-39063-5Published: 21 September 2016
Softcover ISBN: 978-3-319-81809-2Published: 14 June 2018
eBook ISBN: 978-3-319-39065-9Published: 20 September 2016
Series ISSN: 2194-1009
Series E-ISSN: 2194-1017
Edition Number: 1
Number of Pages: XIV, 277
Number of Illustrations: 25 b/w illustrations, 6 illustrations in colour
Topics: Statistical Theory and Methods, Biostatistics, Statistics for Life Sciences, Medicine, Health Sciences