Computational and methodological statistics and biostatistics : contemporary essays in advancement
著者
書誌事項
Computational and methodological statistics and biostatistics : contemporary essays in advancement
（Emerging topics in statistics and biostatistics）
Springer, c2020
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注記
Includes bibliographical references
内容説明・目次
内容説明
In the statistical domain, certain topics have received considerable attention during the last decade or so, necessitated by the growth and evolution of data and theoretical challenges. This growth has invariably been accompanied by computational advancement, which has presented end users as well as researchers with the necessary opportunities to handle data and implement modelling solutions for statistical purposes.
Showcasing the interplay among a variety of disciplines, this book offers pioneering theoretical and applied solutions to practice-oriented problems. As a carefully curated collection of prominent international thought leaders, it fosters collaboration between statisticians and biostatisticians and provides an array of thought processes and tools to its readers. The book thereby creates an understanding and appreciation of recent developments as well as an implementation of these contributions within the broader framework of both academia and industry.
Computational and Methodological Statistics and Biostatistics is composed of three main themes:
* Recent developments in theory and applications of statistical distributions;* Recent developments in supervised and unsupervised modelling;* Recent developments in biostatistics;
and also features programming code and accompanying algorithms to enable readers to replicate and implement methodologies. Therefore, this monograph provides a concise point of reference for a variety of current trends and topics within the statistical domain. With interdisciplinary appeal, it will be useful to researchers, graduate students, and practitioners in statistics, biostatistics, clinical methodology, geology, data science, and actuarial science, amongst others.
目次
1. Computational Issues Of Maximum Likelihood Estimation Of The Skew-T Distribution And A Proposal For The Initialization Of Numerical Optimization - by Adelchi Azzalini (University of Padua, Italy) and Mahdi Salehi (University of Neyshabur, Iran).- 2. Modelling Earthquakes: Characterizing Inter-Arrival Times And Magnitude - by Christophe Ley (Ghent University, Belgium) and Rosaria Simone (University of Naples Frederico II, Italy).- 3. Multivariate Order Statistics Induced By Ordering Linear Combinations Of Components Of Multivariate Elliptical Random Vectors - by Ahad Jamalizadeh (Shahid Bahonar University, Iran), Roohollah Roozegar (Yasouj University, Iran), Narayanaswamy Balakrishnan (McMaster University, Canada) and Mehrdad Naderi (University of Pretoria, South Africa).- 4. Spatial Interpolation Of Extreme PM1 Values Using Copulas - by Alfred Stein, Fakhereh Alidoost and Vera van Zoest (University of Twente, The Netherlands).- 5. Distributional Aspects Of The Condition Number From A Unified Complex Wishart Setting - by Johannes Ferreira and Andriette Bekker (University of Pretoria, South Africa).- 6. Weighted Bivariate Polya-Aeppli Type Ii Distributions - by Claire Geldenhuys and Rene Ehlers (University of Pretoria, South Africa).- 7. On The Distribution Of Linear Combinations Of Chi-Square Random Variables - by Carlos A. Coelho (Universidade Nova de Lisboa, Portugal).- 8. Constructing Multivariate Distributions Using The Dirichlet As A Baseline - by Seite Makgai (University of Pretoria, South Africa), Mohammad Arashi (Shahrood University of Technology, Iran), Daan de Waal (University of the Free State), and Andriette Bekker (University of Pretoria, South Africa).- 9. Evaluating Risk Measures Using The Normal Mean-Variance Birnbaum-Saunders Distribution - by Mehrdad Naderi (University of Pretoria, South Africa), Ahad Jamalizadeh (Shahid Bahonar University, Iran), Wan-Lun Wang (Feng Chia University, Taiwan), Tsung-I Lin (National Chung Hsing University, Taiwan).- 10. On High-Dimensional Multivariate Bayesian Geostatistics - by Sudipto Banerjee (University of California, USA).- 11. On Improving The Performance Of Logistic Regression Analysis Via Extreme Ranking - by Hani M. Samawi (Georgia Southern University, USA).- 12. Optimal Sample Size Allocation For Multi-Level Stress Testing With Extreme Value Regression Under Time Censoring - by Ping Shing Chan (The Chinese University of Hong Kong, Hong Kong),Hon Yiu So (University of Waterloo, Canada), Hon Keung Tony Ng (Southern Methodist University, USA) and Wei Gao (Northeast Normal University, China).- 13. Robust Mixtures Of Scale Mixtures In The Exponential Family - by Frans Kanfer and Sollie Millard (University of Pretoria, South Africa).- 14. Variable Selection Of Interval-Censored Failure Time Data - by Tony Sun (University of Missouri, USA).- 15. On The Design Of A Platform Trial For The Treatment Of Recurrent Clostridium Difficile Infection By Fecal Microbiota Transplantation - by Christine H. Lee (Royal Jubilee Hospital, Canada), Dina Kao (University of Alberta, Canada), Theodore Steiner (University of Vancouver, Canada), Augustine Wigle (University of Guelph, Canada) and Peter T. Kim (University of Guelph, Canada).- 16. Recent Advances In Bayesian Adaptive Designs And Applications - by J. Jack Lee (University of Texas, USA).- 17. Generalizability Theory For Clinician-Rated Outcomes - by Joseph C. Cappelleri (Executive Director of Biostatistics, Pfizer Inc).- 18. Simultaneous Variable Selection And Estimation In Generalized Semiparametric Mixed Effect Modeling Of Longitudinal Data - by Mozhgan Taavoni and Mohammad Arashi (Shahrood University of Technology, Iran).- 19. Generalized Rayleigh-Exponential-Weibull Distribution and its Application to Modelling of Progressive Type-I Interval Censored Data - by Ding-Geng Chen (University of Pretoria) and Y. L. Lio (University of South Dakota).- 20. Applications Of Spatial Statistics In Poverty Alleviation In China - by Yong Ge (State Key Laboratory of Resources and Environmental Information System Institute of Geographical Sciences and Natural Resources Research, China).- 21. Using Improved Robust Estimators In Semiparametric Models For High Dimensional Data - by Mahdi Roozbeh and Mina Norouzirad (Semnan University, Iran).- 22. GMM marginal models with time dependent covariates - by Elsa Vazquez (Arizona State University) and Jeffrey R Wilson (Arizona State University).
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