书目名称 | Robust Rank-Based and Nonparametric Methods | 副标题 | Michigan, USA, April | 编辑 | Regina Y. Liu,Joseph W. McKean | 视频video | | 概述 | 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 l | 丛书名称 | Springer Proceedings in Mathematics & Statistics | 图书封面 |  | 描述 | ..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 | 出版日期 | Conference proceedings 2016 | 关键词 | Bayesian and big data rank-based analyses; Cluster correlated models; General linear models; Nonparamet | 版次 | 1 | doi | https://doi.org/10.1007/978-3-319-39065-9 | isbn_softcover | 978-3-319-81809-2 | isbn_ebook | 978-3-319-39065-9Series ISSN 2194-1009 Series E-ISSN 2194-1017 | issn_series | 2194-1009 | copyright | Springer International Publishing Switzerland 2016 |
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