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

種類:

電子ブック

責任表示:

edited by Regina Y. Liu, Joseph W. McKean

出版情報:

Cham : Springer International Publishing : Imprint: Springer, 2016

著者名:

• Liu, Regina Y.
• McKean, Joseph W.
• SpringerLink (Online service)

シリーズ名:

Springer Proceedings in Mathematics & Statistics ; 168

ISBN:

9783319390659 [3319390651]    

注記:

1 Rank-Based Analysis of Linear Models and Beyond: A Review -- 2 Robust Signed-Rank Variable Selection in Linear Regression -- 3 Generalized Rank-Based Estimates for Linear Models with Cluster Correlated Data -- 4 Iterated Reweighted Rank-Based Estimates for GEE Models -- 5 On the Asymptotic Distribution of a Weighted Least Absolute Deviation Estimate for a Bifurcating Autoregressive Process -- 6 Applications of Robust Regression to “Big” Data Problems -- 7 Rank-Based Inference for Multivariate Data in Factorial Designs -- 8 Two-Sample Rank-Sum Test for Order Restricted Randomized Designs -- 9 On a Partially Sequential Ranked Set Sampling Paradigm -- 10 A New Scale-Invariant Nonparametric Test for Two-Sample Bivariate Location Problem with Application -- 11 Influence Functions and Efficiencies of k-Step Hettmansperger-Randles Estimators for Multivariate Location and Regression -- 12 New Nonparametric Tests for Comparing Multivariate Scales Using Data Depth -- 13 Multivariate Autoregressive Time Series Using S
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 r

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