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E-grāmata: Bayesian Mediation Analysis using R

(University of Leicester, UK)
  • Formāts: 168 pages
  • Izdošanas datums: 04-Jul-2024
  • Izdevniecība: Chapman & Hall/CRC
  • Valoda: eng
  • ISBN-13: 9781040009437
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Bayesian Mediation Analysis using RPalielināt
  • Formāts: 168 pages
  • Izdošanas datums: 04-Jul-2024
  • Izdevniecība: Chapman & Hall/CRC
  • Valoda: eng
  • ISBN-13: 9781040009437
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"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"--

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, and more.



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.

1. Mediation Analysis.
2. Bayesian Mediation Analysis.
3. Parametric Survival Analysis.
4. Competing Risk Modelling.
5. Accelerated Failure Time Modelling.
6. Longitudinal Modelling.
7. High Dimensional Data Analysis.
8. Bayesian Survival Mediation Data Analysis.
9. Bayesian Accelerated Failure Time Mediation Data Analysis.
10. Bayesian Competing Risk Mediation Data Analysis.

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. Bhattacharjees 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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