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Published by Chapman and Hall/CRC, 2024
ISBN 10: 1032287500 ISBN 13: 9781032287508
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 1032287500 ISBN 13: 9781032287508
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Published by Chapman and Hall/CRC, 2024
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Published by Chapman And Hall/CRC Jul 2024, 2024
ISBN 10: 1032287500 ISBN 13: 9781032287508
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Delve into the realm of statistical methodology for mediation analysis with a Bayesian perspective in high dimensional data through this comprehensive guide. Focused on various forms of time-to-event data methodologies, this book helps readers master the application of Bayesian mediation analysis using R. Across ten chapters, this book explores concepts of mediation analysis, survival analysis, accelerated failure time modeling, longitudinal data analysis, and competing risk modeling. Each chapter progressively unravels intricate topics, from the foundations of Bayesian approaches to advanced techniques like variable selection, bivariate survival models, and Dirichlet process priors. With practical examples and step-by-step guidance, this book empowers readers to navigate the intricate landscape of high-dimensional data analysis, fostering a deep understanding of its applications and significance in diverse fields. 170 pp. Englisch.
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Language: English
Published by Chapman And Hall/CRC, 2024
ISBN 10: 1032287500 ISBN 13: 9781032287508
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Delve into the realm of statistical methodology for mediation analysis with a Bayesian perspective in high dimensional data through this comprehensive guide. Focused on various forms of time-to-event data methodologies, this book helps readers master the application of Bayesian mediation analysis using R. Across ten chapters, this book explores concepts of mediation analysis, survival analysis, accelerated failure time modeling, longitudinal data analysis, and competing risk modeling. Each chapter progressively unravels intricate topics, from the foundations of Bayesian approaches to advanced techniques like variable selection, bivariate survival models, and Dirichlet process priors. With practical examples and step-by-step guidance, this book empowers readers to navigate the intricate landscape of high-dimensional data analysis, fostering a deep understanding of its applications and significance in diverse fields.