Methodology In Robust And Nonparametric Statistics
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Methodology in Robust and Nonparametric Statistics
Author | : Jana Jurečková,Pranab Kumar Sen,Jan Picek |
Publsiher | : CRC Press |
Total Pages | : 411 |
Release | : 2012-07-20 |
Genre | : Mathematics |
ISBN | : 9781439840689 |
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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 algorithms, but to work actively with robust and nonparametric procedures, practitioners need to understand their background. Explaining the underpinnings of robust methods and recent theoretical developments, Methodology in Robust and Nonparametric Statistics provides a profound mathematically rigorous explanation of the methodology of robust and nonparametric statistical procedures. Thoroughly up-to-date, this book Presents multivariate robust and nonparametric estimation with special emphasis on affine-equivariant procedures, followed by hypotheses testing and confidence sets Keeps mathematical abstractions at bay while remaining largely theoretical Provides a pool of basic mathematical tools used throughout the book in derivations of main results The methodology presented, with due emphasis on asymptotics and interrelations, will pave the way for further developments on robust statistical procedures in more complex models. Using examples to illustrate the methods, the text highlights applications in the fields of biomedical science, bioinformatics, finance, and engineering. In addition, the authors provide exercises in the text.
Robust Nonparametric Statistical Methods
Author | : Thomas P. Hettmansperger,Joseph W. McKean |
Publsiher | : John Wiley & Sons |
Total Pages | : 492 |
Release | : 1998 |
Genre | : Nonparametric statistics |
ISBN | : STANFORD:36105023161156 |
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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.
Robustness of Statistical Methods and Nonparametric Statistics
Author | : Dieter Rasch,Moti Lal Tiku |
Publsiher | : Springer Science & Business Media |
Total Pages | : 177 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 9789400965287 |
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This volume contains most of the invited and contributed papers presented at the Conference on Robustness of Statistical Methods and Nonparametric Statistics held in the castle oj'Schwerin, Mai 29 - June 4 1983. This conference was organized by the Mathematical Society of the GDR in cooperation with the Society of Physical and Mathematical Biology of the GDR, the GDR-Region of the International Biometric Society and the Academy of Agricultural Sciences of the GDR. All papers included were thoroughly reviewed by scientist listed under the heading "Editorial Collabora tories·'. Some contributions, we are sorry to report, were not recommended for publi cation by the rf'vif'wers and do not appear in these proceedings. The editors thank the reviewers for their valuable comments and suggestions. The conference was organizf'd bv a Programme Committee, its chairman was Prof. Dr. Dieter Rasch (Research Centre of Animal Production, Dummerstorf-Rostock). The members of the Programme Committee were Prof. Dr., Johannes Adam (Martin-Luther-University Halle) Prof. Dr. Heinz Ahrens (Academy of Sciences of the GDR, Berlin) Doz. Dr. Jana Jureckova (Charles University Praha) Prof. Dr. Moti Lal Tiku (McMaster University, Hamilton, Ontario) The aim of the conference was to discuss several aspects of robustness but mainly to present new results regarding the robustness of classical statistical methods especially tests, confidence estimations, and selection procedures, and to compare their perfor mance with nonparametric procedures. Robustness in this sens~ is understood as intensivity against. violation of the normal assumption.
Methodology in Robust and Nonparametric Statistics
Author | : Jana Jureckova,Pranab Sen,Jan Picek |
Publsiher | : CRC Press |
Total Pages | : 410 |
Release | : 2012-07-20 |
Genre | : Mathematics |
ISBN | : 9781439840696 |
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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 Statistics for Applied Research
Author | : Jared A. Linebach,Brian P. Tesch,Lea M. Kovacsiss |
Publsiher | : Springer Science & Business Media |
Total Pages | : 408 |
Release | : 2013-11-19 |
Genre | : Mathematics |
ISBN | : 9781461490418 |
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Non-parametric methods are widely used for studying populations that take on a ranked order (such as movie reviews receiving one to four stars). The use of non-parametric methods may be necessary when data have a ranking but no clear numerical interpretation, such as when assessing preferences. In terms of levels of measurement, non-parametric methods result in "ordinal" data. As non-parametric methods make fewer assumptions, their applicability is much wider than the corresponding parametric methods. In particular, they may be applied in situations where less is known about the application in question. Also, due to the reliance on fewer assumptions, non-parametric methods are more robust. Non-parametric methods have many popular applications, and are widely used in research in the fields of the behavioral sciences and biomedicine. This is a textbook on non-parametric statistics for applied research. The authors propose to use a realistic yet mostly fictional situation and series of dialogues to illustrate in detail the statistical processes required to complete data analysis. This book draws on a readers existing elementary knowledge of statistical analyses to broaden his/her research capabilities. The material within the book is covered in such a way that someone with a very limited knowledge of statistics would be able to read and understand the concepts detailed in the text. The “real world” scenario to be presented involves a multidisciplinary team of behavioral, medical, crime analysis, and policy analysis professionals work together to answer specific empirical questions regarding real-world applied problems. The reader is introduced to the team and the data set, and through the course of the text follows the team as they progress through the decision making process of narrowing the data and the research questions to answer the applied problem. In this way, abstract statistical concepts are translated into concrete and specific language. This text uses one data set from which all examples are taken. This is radically different from other statistics books which provide a varied array of examples and data sets. Using only one data set facilitates reader-directed teaching and learning by providing multiple research questions which are integrated rather than using disparate examples and completely unrelated research questions and data.
Robust Nonparametric Statistical Methods Second Edition
Author | : Thomas P. Hettmansperger,Joseph W. McKean |
Publsiher | : CRC Press |
Total Pages | : 0 |
Release | : 2010-12-20 |
Genre | : Mathematics |
ISBN | : 1439809089 |
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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
Applied Nonparametric Statistical Methods
Author | : Peter Sprent,Nigel C. Smeeton |
Publsiher | : CRC Press |
Total Pages | : 536 |
Release | : 2016-04-19 |
Genre | : Mathematics |
ISBN | : 9781439894019 |
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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
Advanced Robust and Nonparametric Methods in Efficiency Analysis
Author | : Cinzia Daraio,Léopold Simar |
Publsiher | : Springer Science & Business Media |
Total Pages | : 263 |
Release | : 2007-04-10 |
Genre | : Business & Economics |
ISBN | : 9780387352312 |
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Providing a systematic and comprehensive treatment of recent developments in efficiency analysis, this book makes available an intuitive yet rigorous presentation of advanced nonparametric and robust methods, with applications for the analysis of economies of scale and scope, trade-offs in production and service activities, and explanations of efficiency differentials.