Modern Statistical Methods for Spatial and Multivariate Data, Norou Diawara

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: 10480.00 р.
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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: Manly Bryan F.J.
Название: Multivariate Statistical Methods
ISBN: 1498728960 ISBN-13(EAN): 9781498728966
Издательство: Taylor&Francis
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Цена: 8726.00 р.
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Описание:

Multivariate Statistical Methods: A Primer provides an introductory overview of multivariate methods without getting too deep into the mathematical details. This fourth edition is a revised and updated version of this bestselling introductory textbook. It retains the clear and concise style of the previous editions of the book and focuses on examples from biological and environmental sciences. The major update with this edition is that R code has been included for each of the analyses described, although in practice any standard statistical package can be used.

The original idea with this book still applies. This was to make it as short as possible and enable readers to begin using multivariate methods in an intelligent manner. With updated information on multivariate analyses, new references, and R code included, this book continues to provide a timely introduction to useful tools for multivariate statistical analysis.

Автор: Kedem Benjamin Et Al
Название: Statistical Data Fusion
ISBN: 9813200189 ISBN-13(EAN): 9789813200180
Издательство: World Scientific Publishing
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Цена: 12830.00 р.
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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: Bradley Efron and Trevor Hastie
Название: Computer Age Statistical Inference
ISBN: 1107149894 ISBN-13(EAN): 9781107149892
Издательство: Cambridge Academ
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Цена: 9029.00 р.
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Описание: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

Автор: Little
Название: Statistical Analysis with Missing Data, Third Edit ion
ISBN: 0470526793 ISBN-13(EAN): 9780470526798
Издательство: Wiley
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Цена: 12664.00 р.
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Описание: Reflecting new application topics, Statistical Analysis with Missing Data offers a thoroughly up-to-date, reorganized survey of current methodology for handling missing data problems. The third edition reviews historical approaches to the subject and describe rigorous yet simple methods for multivariate analysis with missing values.

Автор: G.N. van Vark; W.W. Howells
Название: Multivariate Statistical Methods in Physical Anthropology
ISBN: 9027717346 ISBN-13(EAN): 9789027717344
Издательство: Springer
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Цена: 16764.00 р.
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Описание: Invited were mathematical statisticians who were known to have an acquaintance with and interest in anthropological problems, together with anthropologists and human geneticists who consider multivariate methodology essential for their research.

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: 20537.00 р.
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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: H. Bozdogan; Arjun K. Gupta
Название: Multivariate Statistical Modeling and Data Analysis
ISBN: 9027725926 ISBN-13(EAN): 9789027725929
Издательство: Springer
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Цена: 20537.00 р.
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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.