Автор: Robert H. Riffenburgh Название: Statistics in Medicine, ISBN: 0123848644 ISBN-13(EAN): 9780123848642 Издательство: Elsevier Science Рейтинг: Цена: 6056 р. Наличие на складе: Невозможна поставка.
Описание: Statistics in Medicine, Third Edition makes medical statistics easy to understand by students, practicing physicians, and researchers. The book begins with databases from clinical medicine and uses such data to give multiple worked-out illustrations of every method. The text opens with how to plan studies from conception to publication and what to do with your data, and follows with step-by-step instructions for biostatistical methods from the simplest levels (averages, bar charts) progressively to the more sophisticated methods now being seen in medical articles (multiple regression, noninferiority testing). Examples are given from almost every medical specialty and from dentistry, nursing, pharmacy, and health care management. A preliminary guide is given to tailor sections of the text to various lengths of biostatistical courses.
Описание: "Research Methods and Statistics in Psychology, sixth edition, provides comprehensive coverage of both quantitative and qualitative methods and statistics. The new edition has been updated to reflect recent software updates and advances in knowledge. This classic research methods text is an essential purchase for all psychology students studying at degree level and beyond"--
Описание: This volume presents the systematic and analytical approaches and strategies from both biostatistics and bioinformatics to the analysis of correlated and high-dimensional data. It poses new challenges and calls for scalable solutions.
Автор: Peacock, Janet, Peacock, Philip Название: Oxford Handbook of Medical Statistics ISBN: 0199551286 ISBN-13(EAN): 9780199551286 Издательство: Oxford Academ Рейтинг: Цена: 3795 р. Наличие на складе: Невозможна поставка.
Описание: The majority of medical research involves quantitative methods and so it is essential to be able to understand and interpret statistics. This book shows readers how to develop the skills required to critically appraise research evidence effectively, and how to conduct research and communicate their findings.
Автор: Richard A. Johnson, Gouri K. Bhattacharyya Название: Statistics: Principles and Methods, 7th Edition ISBN: 0470904119 ISBN-13(EAN): 9780470904114 Издательство: Wiley Рейтинг: Цена: 25790 р. Наличие на складе: Поставка под заказ.
Описание: Statistics: Principles and Methods, 7th Edition provides a comprehensive, accurate introduction to statistics for business professionals who need to learn how to apply key concepts. The chapters include real-world data, designed to make the material more relevant. The numerous examples clearly demonstrate the important points of the methods.
Автор: Peter B?hlmann; Sara van de Geer Название: Statistics for High-Dimensional Data ISBN: 3642268579 ISBN-13(EAN): 9783642268571 Издательство: Springer Рейтинг: Цена: 12539 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This valuable compendium of statistical methods features a unique combination of methodology, theory, algorithms and applications. It covers recently developed approaches to handling large and complex data sets, including the Lasso and boosting methods.
Описание: Proven Methods for Big Data Analysis As big data has become standard in many application areas, challenges have arisen related to methodology and software development, including how to discover meaningful patterns in the vast amounts of data. Addressing these problems, Applied Biclustering Methods for Big and High-Dimensional Data Using R shows how to apply biclustering methods to find local patterns in a big data matrix. The book presents an overview of data analysis using biclustering methods from a practical point of view. Real case studies in drug discovery, genetics, marketing research, biology, toxicity, and sports illustrate the use of several biclustering methods. References to technical details of the methods are provided for readers who wish to investigate the full theoretical background. All the methods are accompanied with R examples that show how to conduct the analyses. The examples, software, and other materials are available on a supplementary website.
Автор: Giraud Название: Introduction to High-Dimensional Statistics ISBN: 1482237946 ISBN-13(EAN): 9781482237948 Издательство: Taylor&Francis Рейтинг: Цена: 6698 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Ever-greater computing technologies have given rise to an exponentially growing volume of data. Today massive data sets (with potentially thousands of variables) play an important role in almost every branch of modern human activity, including networks, finance, and genetics. However, analyzing such data has presented a challenge for statisticians and data analysts and has required the development of new statistical methods capable of separating the signal from the noise. Introduction to High-Dimensional Statistics is a concise guide to state-of-the-art models, techniques, and approaches for handling high-dimensional data. The book is intended to expose the reader to the key concepts and ideas in the most simple settings possible while avoiding unnecessary technicalities. Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, this highly accessible text: Describes the challenges related to the analysis of high-dimensional data Covers cutting-edge statistical methods including model selection, sparsity and the lasso, aggregation, and learning theory Provides detailed exercises at the end of every chapter with collaborative solutions on a wikisite Illustrates concepts with simple but clear practical examples Introduction to High-Dimensional Statistics is suitable for graduate students and researchers interested in discovering modern statistics for massive data. It can be used as a graduate text or for self-study.
Описание: 'Big data' poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. This modern text equips you for the new world - integrating the old and the new, fusing theory and practice and bridging the gap to statistical learning. The theoretical framework includes formal statements that set out clearly the guaranteed 'safe operating zone' for the methods and allow you to assess whether data is in the zone, or near enough. Extensive examples showcase the strengths and limitations of different methods with small classical data, data from medicine, biology, marketing and finance, high-dimensional data from bioinformatics, functional data from proteomics, and simulated data. High-dimension low-sample-size data gets special attention. Several data sets are revisited repeatedly to allow comparison of methods. Generous use of colour, algorithms, Matlab code, and problem sets complete the package. Suitable for master's/graduate students in statistics and researchers in data-rich disciplines.
Описание: There is a growing need for a more automated system of partitioning data sets into groups, or clusters. For example, digital libraries and the World Wide Web continue to grow exponentially, the ability to find useful information increasingly depends on the indexing infrastructure or search engine. Clustering techniques can be used to discover natural groups in data sets and to identify abstract structures that might reside there, without having any background knowledge of the characteristics of the data. Clustering has been used in a variety of areas, including computer vision, VLSI design, data mining, bio-informatics (gene expression analysis), and information retrieval, to name just a few. This book focuses on a few of the most important clustering algorithms, providing a detailed account of these major models in an information retrieval context. The beginning chapters introduce the classic algorithms in detail, while the later chapters describe clustering through divergences and show recent research for more advanced audiences.
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