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Titlebook: Robust Rank-Based and Nonparametric Methods; Michigan, USA, April Regina Y. Liu,Joseph W. McKean Conference proceedings 2016 Springer Inter

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書目名稱Robust Rank-Based and Nonparametric Methods
副標題Michigan, USA, April
編輯Regina Y. Liu,Joseph W. McKean
視頻videohttp://file.papertrans.cn/832/831351/831351.mp4
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
叢書名稱Springer Proceedings in Mathematics & Statistics
圖書封面Titlebook: Robust Rank-Based and Nonparametric Methods; Michigan, USA, April Regina Y. Liu,Joseph W. McKean Conference proceedings 2016 Springer Inter
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
關(guān)鍵詞Bayesian and big data rank-based analyses; Cluster correlated models; General linear models; Nonparamet
版次1
doihttps://doi.org/10.1007/978-3-319-39065-9
isbn_softcover978-3-319-81809-2
isbn_ebook978-3-319-39065-9Series ISSN 2194-1009 Series E-ISSN 2194-1017
issn_series 2194-1009
copyrightSpringer International Publishing Switzerland 2016
The information of publication is updating

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New Nonparametric Tests for Comparing Multivariate Scales Using Data Depth,ing but significantly outperform the parametric one in the non-normal settings. As an illustration, the proposed tests are applied to analyze an airline performance dataset collected by the FAA in the context of comparing the performance stability of airlines.
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Median Stable Distributions,ional of the data’s cdf so that the analysis of median stability involves solutions to functional equations (as opposed to sums of random variables). A few properties of median stable distributions are presented including their relation to the limiting distribution of the remedian.
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Generalized Rank-Based Estimates for Linear Models with Cluster Correlated Data,dent, however, within a cluster the responses are allowed to be dependent. The method is applicable to general within cluster error structure. Application of a model which assumes the within cluster errors which follow an AR(1) process is developed. Discussion of an estimate of the AR(1) parameter i
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Iterated Reweighted Rank-Based Estimates for GEE Models, on a set of generalized estimating equations (GEEs) for regression parameters, that specify only the relationship between the marginal mean of the response variable and covariates. Their solution is based on iterated reweighted least squares fitting. In this paper, we propose a rank-based fitting p
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