The Statistical Analysis of Multivariate Failure Time Data, Prentice, Ross L.

Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman
Название: The Elements of Statistical Learning
ISBN: 0387848576 ISBN-13(EAN): 9780387848570
Издательство: Springer
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Цена: 69870.00 T
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Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.

Автор: Jianguo Sun
Название: The Statistical Analysis of Interval-censored Failure Time Data
ISBN: 1441921923 ISBN-13(EAN): 9781441921925
Издательство: Springer
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Цена: 153720.00 T
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Описание: This book collects and unifies statistical models and methods that have been proposed for analyzing interval-censored failure time data. It provides the first comprehensive coverage of the topic and complements the books on right-censored data.

Автор: T. W. Anderson
Название: An Introduction to Multivariate Statistical Analysis, Third Edition
ISBN: 0471360910 ISBN-13(EAN): 9780471360919
Издательство: Wiley
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Цена: 184750.00 T
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Описание: Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures. This work treats the basic and important topics in multivariate statistics.

Автор: V.I. Serdobolskii
Название: Multivariate Statistical Analysis
ISBN: 0792366433 ISBN-13(EAN): 9780792366430
Издательство: Springer
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Цена: 121110.00 T
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Описание: Presents a branch of mathematical statistics which intends to construct unimprovable methods of multivariate analysis, multi-parametric estimation, and discriminant and regression analysis. This work is suitable for researchers and graduate students whose work involves statistics and probability, reliability and risk analysis, and econometrics.

Автор: Kedem Benjamin Et Al
Название: Statistical Data Fusion
ISBN: 9813200189 ISBN-13(EAN): 9789813200180
Издательство: World Scientific Publishing
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Цена: 85530.00 T
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Описание: 'The book provides a comprehensive review of the DRM approach to data fusion. It is well written and easy to follow, although the technical details are not trivial. The authors did an excellent job in making a concise introduction of the statistical techniques in data fusion. The book contains several real data ... Overall, I found that the book covers an important topic and the DRM is a promising tool in this area. Researchers on data fusion will surely find this book very helpful and I will use this book in studying with my PhD students.'Journal of the American Statistical AssociationThis book comes up with estimates or decisions based on multiple data sources as opposed to more narrowly defined estimates or decisions based on single data sources. And as the world is awash with data obtained from numerous and varied processes, there is a need for appropriate statistical methods which in general produce improved inference by multiple data sources.The book contains numerous examples useful to practitioners from genomics. Topics range from sensors (radars), to small area estimation of body mass, to the estimation of small tail probabilities, to predictive distributions in time series analysis.

Автор: H. Bozdogan; Arjun K. Gupta
Название: Multivariate Statistical Modeling and Data Analysis
ISBN: 9401082642 ISBN-13(EAN): 9789401082648
Издательство: Springer
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Цена: 136910.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The areas covered included topics in the analysis of repeated measurements, cluster analysis, discriminant analysis, canonical cor- relations, distribution theory and testing, bivariate densi ty estimation, factor analysis, principle component analysis, multidimensional scaling, multivariate linear models, nonparametric regression, etc.

Автор: Nussbaum E Michael
Название: Categorical and Nonparametric Data Analysis
ISBN: 1138787825 ISBN-13(EAN): 9781138787827
Издательство: Taylor&Francis
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Цена: 81650.00 T
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Описание: Featuring in-depth coverage of categorical and nonparametric statistics, this book provides a conceptual framework for choosing the most appropriate type of test in various research scenarios. Class tested at the University of Nevada, the book's clear explanations of the underlying assumptions, computer simulations, and Exploring the Concept boxes help reduce reader anxiety. Problems inspired by actual studies provide meaningful illustrations of the techniques. The underlying assumptions of each test and the factors that impact validity and statistical power are reviewed so readers can explain their assumptions and how tests work in future publications. Numerous examples from psychology, education, and other social sciences demonstrate varied applications of the material. Basic statistics and probability are reviewed for those who need a refresher. Mathematical derivations are placed in optional appendices for those interested in this detailed coverage. Highlights include the following: Unique coverage of categorical and nonparametric statistics better prepares readers to select the best technique for their particular research project; however, some chapters can be omitted entirely if preferred. Step-by-step examples of each test help readers see how the material is applied in a variety of disciplines.  Although the book can be used with any program, examples of how to use the tests in SPSS and Excel foster conceptual understanding. Exploring the Concept boxes integrated throughout prompt students to review key material and draw links between the concepts to deepen understanding.  Problems in each chapter help readers test their understanding of the material.  Emphasis on selecting tests that maximize power helps readers avoid "marginally" significant results.  Website (www.routledge.com/9781138787827) features datasets for the book's examples and problems, and for the instructor, PowerPoint slides, sample syllabi, answers to the even-numbered problems, and Excel data sets for lecture purposes. Intended for individual or combined graduate or advanced undergraduate courses in categorical and nonparametric data analysis, cross-classified data analysis, advanced statistics and/or quantitative techniques taught in psychology, education, human development, sociology, political science, and other social and life sciences, the book also appeals to researchers in these disciplines. The nonparametric chapters can be deleted if preferred. Prerequisites include knowledge of t tests and ANOVA.

Автор: H. Bozdogan; Arjun K. Gupta
Название: Multivariate Statistical Modeling and Data Analysis
ISBN: 9027725926 ISBN-13(EAN): 9789027725929
Издательство: Springer
Рейтинг:
Цена: 136910.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The areas covered included topics in the analysis of repeated measurements, cluster analysis, discriminant analysis, canonical cor- relations, distribution theory and testing, bivariate densi ty estimation, factor analysis, principle component analysis, multidimensional scaling, multivariate linear models, nonparametric regression, etc.

Автор: Kawaguchi, Atsushi
Название: Multivariate Analysis for Neuroimaging Data
ISBN: 0367255324 ISBN-13(EAN): 9780367255329
Издательство: Taylor&Francis
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Цена: 158230.00 T
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Описание: This book enables us to analyze statistically brain imaging data. It is meant for a wide range of researchers interested in biostatistics, data science, and neuroscience. It is useful to understand the background theory of standard software for neuroimaging data analysis.

Автор: Warton, David I
Название: Eco-stats - data analysis in ecology
ISBN: 3030884422 ISBN-13(EAN): 9783030884420
Издательство: Springer
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Цена: 111790.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book introduces ecologists to the wonderful world of modern tools for data analysis, especially multivariate analysis. For biologists with relatively little prior knowledge of statistics, it introduces a modern, advanced approach to data analysis in an intuitive and accessible way.

Автор: Koch
Название: Analysis of Multivariate and High-Dimensional Data
ISBN: 0521887933 ISBN-13(EAN): 9780521887939
Издательство: Cambridge Academ
Рейтинг:
Цена: 70750.00 T
Наличие на складе: Поставка под заказ.
Описание: `Big data` poses challenges that require both classical multivariate methods and modern machine-learning techniques. This coherent treatment integrates theory with data analysis, visualisation and interpretation of the analysis. Problems, data sets and MATLAB (R) code complete the package. It is suitable for master`s/graduate students in statistics and working scientists in data-rich disciplines.

Автор: Zadora Grzegorz
Название: Statistical Analysis in Forensic Science
ISBN: 0470972106 ISBN-13(EAN): 9780470972106
Издательство: Wiley
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Цена: 81260.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides the methods and software to enable statisticians and forensic experts to work effectively with evidence evaluation methods. It also covers a collection of recent likelihood ratio (LR) approaches, explores suitable software toolboxes, and includes documentation and examples about how to use them in practice.