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Bayesian Mediation Analysis using R

Atanu Bhattacharjee (University of Leicester, UK)

$221

Hardback

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English
Chapman & Hall/CRC
04 July 2024
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.
By:  
Imprint:   Chapman & Hall/CRC
Country of Publication:   United Kingdom
Dimensions:   Height: 234mm,  Width: 156mm, 
Weight:   453g
ISBN:   9781032287508
ISBN 10:   1032287500
Pages:   154
Publication Date:  
Audience:   College/higher education ,  Professional and scholarly ,  Primary ,  Undergraduate
Format:   Hardback
Publisher's Status:   Active

Dr. Atanu Bhattacharjee serves as an Academic Statistician at the University of Dundee, Scotland, specializing in medical statistics. His expertise encompasses survival analysis, competing risks, and high-dimensional data analysis. Dr. Bhattacharjee’s research revolves around advancing statistical methodologies for analyzing time-to-event data, particularly emphasizing competing risks and high-dimensional data. His contributions are evident through numerous publications in esteemed statistical journals. Additionally, Dr. Bhattacharjee has played a pivotal role in developing an R package tailored for conducting competing risks analysis and high dimensional data analysis.

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