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Fundamental statistical methods for analysis of Alzheimer's and other neurodegenerative diseases / : библиография

Полный текст
База данных: Каталог библиотеки СФУ (616.831100727  F97)
Библиографическое описание: Irimata, Katherine E. ,. Fundamental statistical methods for analysis of Alzheimer's and other neurodegenerative diseases / : библиография / Katherine E., Irimata ; author.: (Jeffrey R.), Wilson, Jeffrey, (Brittany Nicole), Dugger, Brittany N. - Электрон. текстовые дан. - [S. l. : s. n.], 2020. - 1 online resource. - Загл. с титул. экрана. - Includes bibliographical references and index. - ISBN 9781421436722. - ISBN 1421436728 : Б. ц. - Текст : электронный. Примечания о происхождении: Коллекция цифровых книг Ebsco ebook (централизованная подписка 2023 г., бессрочный доступ)., НБ СФУ
Аннотация: "This book explains statistical techniques commonly used in analyzing data for Alzheimer's and other neurodegenerative diseases, and it presents examples from real-world applications in an effort to make the techniques useful for professionals and students. The book leads readers through the steps of conducting multivariate analyses while adjusting for correlation or the hierarchical structure of data in prediction and inferences. Techniques such as spatial analysis, Bayesian analysis, and time-dependent covariates are included. Several data sets from the National Alzheimer's Coordinating Center are analyzed with statistical software commonly used by Alzheimer's researchers, and the results are shown to readers by way of illustration"--
Год издания: 2020
Авторы: Irimata Katherine E.,
Редакторы, составители и др.: Wilson, Jeffrey (Jeffrey R.), (author.); Dugger, Brittany N. (Brittany Nicole), (author.)
Количество экземпляров:
Издание имеется только в электронном виде

Цифровые и тематические книжные коллекции издания

Содержание

  • Introduction to Statistical Software and Alzheimer's Data
  • Review of Introductory Statistical Methods
  • Generalized Linear Models
  • Hierarchical Regression Models for Continuous Responses
  • Hierarchical Logistic Regression Models
  • Bayesian Regression Models
  • Multiple Membership Models
  • Survival Data Analysis
  • Modeling Responses with Time-Dependent Covariates
  • Joint Modeling of Mean and Dispersion
  • Neural Networks and Other Machine Learning Techniques for Big Data
  • Case Study.

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