Robust Rank-Based and Nonparametric Methods : Michigan, USA, April 2015: Selected, Revised, and Extended Contributions / edited by Regina Y. Liu, Joseph W. McKean.

Format:

Book

Contributor:

Liu, Regina Y., Editor.

McKean, Joseph W., Editor.

Series:

Springer Proceedings in Mathematics & Statistics, 2194-1017 ; 168

Language:

English

Subjects (All):

Statistics.

Biometry.

Statistical Theory and Methods.

Biostatistics.

Local Subjects:

Statistical Theory and Methods.

Biostatistics.

Physical Description:

1 online resource (XIV, 277 p. 31 illus., 6 illus. in color.)

Edition:

1st ed. 2016.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2016.

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.

Contents:

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 Schweppe Weighted Wilcoxon Estimates

14 Median Stable Distributions

15 Confidence Intervals for Mean Difference between Two Delta-distributions.

Notes:

Includes bibliographical references at the end of each chapters and indexes.

ISBN:

3-319-39065-1