Robust rank-based and nonparametric methods : Michigan, USA, April 2015 : selected, revised, and extended contributions / Regina Y. Liu, Joseph W. McKean, editors.
2016
QA278.8
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Title
Robust rank-based and nonparametric methods : Michigan, USA, April 2015 : selected, revised, and extended contributions / Regina Y. Liu, Joseph W. McKean, editors.
Meeting Name
International Conference on Robust Rank-Based and Nonparametric Methods (2015 : Kalamazoo, Michigan)
ISBN
9783319390659 (electronic book)
3319390651 (electronic book)
9783319390635
3319390651 (electronic book)
9783319390635
Published
Switzerland : Springer, [2016]
Language
English
Description
1 online resource.
Call Number
QA278.8
Dewey Decimal Classification
519.5/4
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (viewed September 27, 2016).
Series
Springer proceedings in mathematics & statistics ; v. 168.
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