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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.
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.
Presenting an extensive set of tools and methods for data analysis,
Robust Nonparametric Statistical Methods, Second Edition covers
univariate tests and estimates with extensions to linear models,
multivariate models, times series models, experimental designs, and
mixed models. It follows the approach of the first edition by
developing rank-based methods from the unifying theme of geometry.
This edition, however, includes more models and methods and
significantly extends the possible analyses based on ranks. New to
the Second Edition * A new section on rank procedures for nonlinear
models * A new chapter on models with dependent error structure,
covering rank methods for mixed models, general estimating
equations, and time series * New material on the development of
computationally efficient affine invariant/equivariant sign methods
based on transform-retransform techniques in multivariate models
Taking a comprehensive, unified approach to statistical analysis,
the book continues to describe one- and two-sample problems, the
basic development of rank methods in the linear model, and fixed
effects experimental designs. It also explores models with
dependent error structure and multivariate models. The authors
illustrate the implementation of the methods using many real-world
examples and R. More information about the data sets and R packages
can be found at www.crcpress.com
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