Springer Proceedings in Mathematics & Statistics

Robust Rank-Based and Nonparametric Methods

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

Editors: Liu, Regina Y., McKean, Joseph W. (Eds.)

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  • 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
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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. 

About the authors

Dr. Regina Liu is currently Distinguished Professor of Statistics at Rutgers University, USA. She received her Ph.D. from Columbia University at New York. She has published extensively in a broad range of research areas, including nonparametric statistics, data depth, robust statistics, resampling techniques, text mining, fusion learning, statistical quality control, and aviation risk management. She has served on the editorial board of several statistical journals, including The Annals of Statistics, Journal of American Statistical Association, and Journal of Multivariate Analysis. She is the recipient of the 2011 Stieltjes Professor, Thomas Stieltjes Institute for Mathematics, the Netherlands. She has been elected fellow of American Statistical Association, Institute of Mathematical Statistics, and International Statistical Institute.

Dr. Joseph McKean is Professor of Statistics at Western Michigan University. He received his PhD 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.

Table of contents (15 chapters)

Table of contents (15 chapters)
  • Rank-Based Analysis of Linear Models and Beyond: A Review

    Pages 1-24

    McKean, Joseph W. (et al.)

  • Robust Signed-Rank Variable Selection in Linear Regression

    Pages 25-45

    Abebe, Asheber (et al.)

  • Generalized Rank-Based Estimates for Linear Models with Cluster Correlated Data

    Pages 47-60

    Kloke, John

  • Iterated Reweighted Rank-Based Estimates for GEE Models

    Pages 61-79

    Abebe, Asheber (et al.)

  • On the Asymptotic Distribution of a Weighted Least Absolute Deviation Estimate for a Bifurcating Autoregressive Process

    Pages 81-100

    Terpstra, Jeff T.

Buy this book

eBook $149.00
price for USA in USD
  • ISBN 978-3-319-39065-9
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • Immediate eBook download after purchase and usable on all devices
  • Bulk discounts available
Hardcover $199.99
price for USA in USD
Softcover $199.99
price for USA in USD
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Bibliographic Information

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
Series Volume
168
Copyright
2016
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing Switzerland
eBook ISBN
978-3-319-39065-9
DOI
10.1007/978-3-319-39065-9
Hardcover ISBN
978-3-319-39063-5
Softcover ISBN
978-3-319-81809-2
Series ISSN
2194-1009
Edition Number
1
Number of Pages
XIV, 277
Number of Illustrations
25 b/w illustrations, 6 illustrations in colour
Topics