Robust Rank-Based and Nonparametric Methods - Michigan, USA, April 2015: Selected, Revised, and Extended Contributions (Hardcover, 1st ed. 2016)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Springer Proceedings in Mathematics & Statistics, 168
Release date:	September 2016
First published:	2016
Editors:	Regina Y. Liu • Joseph W. McKean
Dimensions:	235 x 155 x 18mm (L x W x T)
Format:	Hardcover
Pages:	277
Edition:	1st ed. 2016
ISBN-13:	978-3-319-39063-5
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Science & Mathematics > Biology, life sciences > Life sciences: general issues > General
LSN:	3-319-39063-5
Barcode:	9783319390635

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