• Produktbild: Bayesian Inference and Computation in Reliability and Survival Analysis
  • Produktbild: Bayesian Inference and Computation in Reliability and Survival Analysis

Bayesian Inference and Computation in Reliability and Survival Analysis

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

02.08.2023

Abbildungen

XVIII, 364 p. 54 illus., 30 illus. in color.

Herausgeber

Yuhlong Lio + weitere

Verlag

Springer

Seitenzahl

364

Maße (L/B/H)

23,5/15,5/2,1 cm

Gewicht

581 g

Auflage

23001 Auflage 1st edition 2022

Sprache

Englisch

ISBN

978-3-030-88660-8

Beschreibung

Portrait


Dr. Yuhlong Lio 
is a professor with the Department of Mathematical Sciences at the University of South Dakota, Vermillion, SD, USA. He is an associate editor of professional journals, including the
Journal of Statistical Computation and Simulation
. He is the co-editor of
Statistical Modeling for Degradation Data
 and
Statistical Quality Technologies: Theory and Practice
. His research interests include reliability, quality control, censoring methodology, kernel smoothing estimation, and accelerated degradation data modelling. Dr. Lio has more than 100 refereed publications.


Dr. Ding-Geng Chen
is a fellow of the American Statistical Association and currently the executive director and professor in biostatistics at Arizona State University. He was the Wallace Kuralt Distinguished Professor at the University of North Carolina at Chapel Hill, a professor in biostatistics at the University of Rochester, and the Karl E. Peace Endowed Eminent Scholar Chair in Biostatistics at Georgia Southern University. He is also a senior statistics consultant for biopharmaceuticals and government agencies with extensive expertise in Monte Carlo simulations, clinical trial biostatistics, and public health statistics. Dr. Chen has more than 200 refereed professional publications, and he has co-authored and co-edited 33 books on clinical trial methodology, meta-analysis, and public health applications. He has been invited nationally and internationally to give speeches on his research. Dr. Chen was honored with the "Award of Recognition" in 2014 by the Deming Conference Committee for highly successful advanced biostatistics workshop tutorials with his books.


Dr. Hon Keung Tony Ng
 is currently a professor of statistical science with Southern Methodist University, Dallas, TX, USA. He is an associate editor of
Communications in Statistics
,
Computational Statistics
,
IEEE Transactions on Reliability
,
Journal of Statistical Computation and Simulation
,
Naval Research Logistics
,
Sequential Analysis
and
Statistics and Probability Letters
. His research interests include reliability, censoring methodology, ordered data analysis, nonparametric methods, and statistical inference. He has published more than 140 research papers in refereed journals. He is the co-author of the book
Precedence-Type Tests and Applications
and co-editor of
Statistical Modeling for Degradation Data
,
Statistical Quality Technologies: Theory and Practice
,
Ordered Data Analysis,
and
Modeling and Health Research Methods
. Professor Ng is a fellow of the American Statistical Association, an elected senior member of IEEE, and an elected member of the International Statistical Institute.


Dr. Tzong-Ru Tsai
 is currently the Dean of the College of Business and Management and a professor in the Department of Statistics at Tamkang University inNew Taipei City, Taiwan. His main research interests include quality control, reliability analysis, and machine learning. He has served as a consultant with extensive expertise in statistical quality control and experimental design for many companies in the past years. He is the co-editor of
Statistical Modeling for Degradation Data
 and
Statistical Quality Technologies: Theory and Practice
. He is an associate editor of the 
Journal of Statistical Computation and Simulation
 and
Mathematics
. Dr. Tsai has more than 100 refereed publications.




Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

02.08.2023

Abbildungen

XVIII, 364 p. 54 illus., 30 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

364

Maße (L/B/H)

23,5/15,5/2,1 cm

Gewicht

581 g

Auflage

23001 Auflage 1st edition 2022

Sprache

Englisch

ISBN

978-3-030-88660-8

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Bayesian Inference and Computation in Reliability and Survival Analysis
  • Produktbild: Bayesian Inference and Computation in Reliability and Survival Analysis

  • 1. 
    A Bayesian Approach for Step-stress Accelerated Life-tests for One-shot Devices under Exponential Distributions.- 2. 
    Bayesian Estimation of Stress-strength Parameter for Moran-Downton Bivariate Exponential Distribution under Progressive Type-II Censoring.- 3. 
    Bayesian Computation in A Birnbaum-Saunders Reliability Model with Applications to Fatigue Data.- 4. 
    A Competing Risks Model Based on A Two-parameter Exponential Family Distribution under Progressive Type-II Censoring.- 5. 
    Bayesian Computations for Reliability Analysis in Dynamic Environments.- 6. 
    Bayesian Analysis of Stochastic Processes in Reliability.- 7. 
    Bayesian Analysis of A New Bivariate Wiener Degradation Process.- 8. 
    Bayesian Estimation for Bivariate Gamma Processes with Copula.- 9. 
    Review of Statistical Treatment for Oncology Dose Escalation Trial with Prolonged Evaluation Window or Fast Enrollment.- 10. 
    A Bayesian Approach for the Analysis of Tumorigenicity Data from Sacrificial Experiments under Weibull Lifetimes.- 11. 
    Bayesian Sensitivity Analysis in Survival and Longitudinal Trial with Missing Data.- 12. 
    Bayesian Analysis for Clustered Data under A Semi-competing Risks Framework.- 13. 
    Survival Analysis for the Inverse Gaussian Distribution: Natural Conjugate and Jeffrey’s Priors.- 14. 
    Bayesian Inferences for Panel Count Data and Interval-censored Data with Nonparametric Modeling of the Baseline Functions.- 15. 
    Bayesian Approach for Interval-censored Survival Data with Time-varying Coefficients.- 16. 
    Bayesian Approach for Joint-modeling Longitudinal Data and Survival Data Simultaneously in Public Health Studies