[PDF] Robust Nonparametric Statistical Methods - eBooks Review

Robust Nonparametric Statistical Methods


Robust Nonparametric Statistical Methods
DOWNLOAD

Download Robust Nonparametric Statistical Methods PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Robust Nonparametric Statistical Methods book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page



Robust Nonparametric Statistical Methods


Robust Nonparametric Statistical Methods
DOWNLOAD
Author : Thomas P. Hettmansperger
language : en
Publisher: CRC Press
Release Date : 2010-12-20

Robust Nonparametric Statistical Methods written by Thomas P. Hettmansperger and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-12-20 with Mathematics categories.


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 m



Robust Nonparametric Statistical Methods


Robust Nonparametric Statistical Methods
DOWNLOAD
Author : Thomas P. Hettmansperger
language : en
Publisher: John Wiley & Sons
Release Date : 1998

Robust Nonparametric Statistical Methods written by Thomas P. Hettmansperger and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Mathematics categories.


Offering an alternative to traditional statistical procedures which are based on least squares fitting, the authors cover such topics as one and two sample location models, linear models, and multivariate models. Both theory and applications are examined.



Robust Nonparametric Statistical Methods Monographs On Statistics And Applied Probability 119


Robust Nonparametric Statistical Methods Monographs On Statistics And Applied Probability 119
DOWNLOAD
Author : THOMAS P. HETTMANSPERGER
language : en
Publisher:
Release Date : 2011

Robust Nonparametric Statistical Methods Monographs On Statistics And Applied Probability 119 written by THOMAS P. HETTMANSPERGER and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.




Nonparametric Statistical Methods Using R


Nonparametric Statistical Methods Using R
DOWNLOAD
Author : John Kloke
language : en
Publisher: CRC Press
Release Date : 2024-05-20

Nonparametric Statistical Methods Using R written by John Kloke and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-20 with Mathematics categories.


Praise for the first edition: “This book would be especially good for the shelf of anyone who already knows nonparametrics, but wants a reference for how to apply those techniques in R.” -The American Statistician This thoroughly updated and expanded second edition of Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses. Two new chapters covering multivariate analyses and big data have been added. Core classical nonparametrics chapters on one- and two-sample problems have been expanded to include discussions on ties as well as power and sample size determination. Common machine learning topics --- including k-nearest neighbors and trees --- have also been included in this new edition. Key Features: Covers a wide range of models including location, linear regression, ANOVA-type, mixed models for cluster correlated data, nonlinear, and GEE-type. Includes robust methods for linear model analyses, big data, time-to-event analyses, timeseries, and multivariate. Numerous examples illustrate the methods and their computation. R packages are available for computation and datasets. Contains two completely new chapters on big data and multivariate analysis. The book is suitable for advanced undergraduate and graduate students in statistics and data science, and students of other majors with a solid background in statistical methods including regression and ANOVA. It will also be of use to researchers working with nonparametric and rank-based methods in practice.



Applied Nonparametric Statistical Methods


Applied Nonparametric Statistical Methods
DOWNLOAD
Author : Peter Sprent
language : en
Publisher: CRC Press
Release Date : 2016-04-19

Applied Nonparametric Statistical Methods written by Peter Sprent and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-19 with Mathematics categories.


While preserving the clear, accessible style of previous editions, Applied Nonparametric Statistical Methods, Fourth Edition reflects the latest developments in computer-intensive methods that deal with intractable analytical problems and unwieldy data sets. Reorganized and with additional material, this edition begins with a brief summary of some



Applied Nonparametric Statistical Methods


Applied Nonparametric Statistical Methods
DOWNLOAD
Author : Nigel Smeeton
language : en
Publisher: CRC Press
Release Date : 2025-03-31

Applied Nonparametric Statistical Methods written by Nigel Smeeton and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-31 with Mathematics categories.


Nonparametric statistical methods minimize the number of assumptions that need to be made about the distribution of data being analysed, unlike classical parametric methods. As such, they are an essential part of a statistician’s armoury, and this book is an essential resource in their application. Starting from the basics of statistics, it takes the reader through the main nonparametric approaches with an emphasis on carefully explained examples backed up by use of the R programming language. Key features of this fully revised and extended fifth edition include the following: An introductory chapter that provides a gentle introduction to the basics of statistics, including types of data, hypothesis testing, confidence intervals and ethical issues An R package containing functions that have been written for the examples in the text and the exercises Summary bullet points at the end of each section to enable the reader to locate important principles quickly A case study from medical research to demonstrate nonparametric approaches to the data analysis Examples fully integrated into the text, drawn from published research on contemporary issues, with more detail given in their explanation Extensive exercises along with complete solutions that allow the reader to test their understanding of the material Articles used in the examples and exercises carefully chosen to enable readers to identify up-to-date literature in their field for research, publications and teaching material Numerous historical references throughout the text, from which to explore the origins of nonparametric methods Applied Nonparametric Statistical Methods, Fifth Edition, is a comprehensive course text in nonparametric techniques suitable for undergraduate students of mathematics and statistics. It assumes only basic previous experience of statistics, and with algebra kept to a minimum, it is also ideal for quantitative methods modules delivered to undergraduate or postgraduate students in science, business and health service training. It is an invaluable resource for researchers, medical practitioners, business managers, research and development staff, and others needing to interpret quantitative information. Suitable for self-directed learning in continuing professional development, it also acts as a handy accessible reference manual.



Methodology In Robust And Nonparametric Statistics


Methodology In Robust And Nonparametric Statistics
DOWNLOAD
Author : Jana Jureckova
language : en
Publisher: CRC Press
Release Date : 2012-07-20

Methodology In Robust And Nonparametric Statistics written by Jana Jureckova and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-07-20 with Mathematics categories.


Robust and nonparametric statistical methods have their foundation in fields ranging from agricultural science to astronomy, from biomedical sciences to the public health disciplines, and, more recently, in genomics, bioinformatics, and financial statistics. These disciplines are presently nourished by data mining and high-level computer-based algo



Nonparametric Statistical Methods


Nonparametric Statistical Methods
DOWNLOAD
Author : Myles Hollander
language : en
Publisher: Wiley-Interscience
Release Date : 1999-01-25

Nonparametric Statistical Methods written by Myles Hollander and has been published by Wiley-Interscience this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-01-25 with Mathematics categories.


An extensive array of examples drawn from actual experiments illustrates clearly how to use nonparametric approaches to handle one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems.



Innovative Statistical Methods For Public Health Data


Innovative Statistical Methods For Public Health Data
DOWNLOAD
Author : Ding-Geng (Din) Chen
language : en
Publisher: Springer
Release Date : 2015-08-31

Innovative Statistical Methods For Public Health Data written by Ding-Geng (Din) Chen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-08-31 with Medical categories.


The book brings together experts working in public health and multi-disciplinary areas to present recent issues in statistical methodological development and their applications. This timely book will impact model development and data analyses of public health research across a wide spectrum of analysis. Data and software used in the studies are available for the reader to replicate the models and outcomes. The fifteen chapters range in focus from techniques for dealing with missing data with Bayesian estimation, health surveillance and population definition and implications in applied latent class analysis, to multiple comparison and meta-analysis in public health data. Researchers in biomedical and public health research will find this book to be a useful reference and it can be used in graduate level classes.



The Oxford Handbook Of Quantitative Methods Volume 1 Foundations


The Oxford Handbook Of Quantitative Methods Volume 1 Foundations
DOWNLOAD
Author : Todd D. Little
language : en
Publisher: Oxford University Press
Release Date : 2013-01-15

The Oxford Handbook Of Quantitative Methods Volume 1 Foundations written by Todd D. Little and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-01-15 with Psychology categories.


Research today demands the application of sophisticated and powerful research tools. Fulfilling this need, The Oxford Handbook of Quantitative Methods is the complete tool box to deliver the most valid and generalizable answers to todays complex research questions. It is a one-stop source for learning and reviewing current best-practices in quantitative methods as practiced in the social, behavioral, and educational sciences. Comprising two volumes, this handbook covers a wealth of topics related to quantitative research methods. It begins with essential philosophical and ethical issues related to science and quantitative research. It then addresses core measurement topics before delving into the design of studies. Principal issues related to modern estimation and mathematical modeling are also detailed. Topics in the handbook then segway into the realm of statistical inference and modeling with chapters dedicated to classical approaches as well as modern latent variable approaches. Numerous chapters associated with longitudinal data and more specialized techniques round out this broad selection of topics. Comprehensive, authoritative, and user-friendly, this two-volume set will be an indispensable resource for serious researchers across the social, behavioral, and educational sciences.