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Monte-carlo simulation-based statistical modeling

This book brings together expert researchers engaged in Monte-Carlo simulation-based statistical modeling, offering them a forum to present and discuss recent issues in methodological development as well as public health applications. It is divided into three parts, with the first providing an overv...

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Detalles Bibliográficos
Autores principales: Chen, Ding-Geng, Chen, John
Lenguaje:eng
Publicado: Springer 2017
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-981-10-3307-0
http://cds.cern.ch/record/2683158
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author Chen, Ding-Geng
Chen, John
author_facet Chen, Ding-Geng
Chen, John
author_sort Chen, Ding-Geng
collection CERN
description This book brings together expert researchers engaged in Monte-Carlo simulation-based statistical modeling, offering them a forum to present and discuss recent issues in methodological development as well as public health applications. It is divided into three parts, with the first providing an overview of Monte-Carlo techniques, the second focusing on missing data Monte-Carlo methods, and the third addressing Bayesian and general statistical modeling using Monte-Carlo simulations. The data and computer programs used here will also be made publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, and to readily apply them in their own research. Featuring highly topical content, the book has the potential to impact model development and data analyses across a wide spectrum of fields, and to spark further research in this direction.
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spelling cern-26831582021-04-21T18:21:29Zdoi:10.1007/978-981-10-3307-0http://cds.cern.ch/record/2683158engChen, Ding-GengChen, JohnMonte-carlo simulation-based statistical modelingMathematical Physics and MathematicsThis book brings together expert researchers engaged in Monte-Carlo simulation-based statistical modeling, offering them a forum to present and discuss recent issues in methodological development as well as public health applications. It is divided into three parts, with the first providing an overview of Monte-Carlo techniques, the second focusing on missing data Monte-Carlo methods, and the third addressing Bayesian and general statistical modeling using Monte-Carlo simulations. The data and computer programs used here will also be made publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, and to readily apply them in their own research. Featuring highly topical content, the book has the potential to impact model development and data analyses across a wide spectrum of fields, and to spark further research in this direction.Springeroai:cds.cern.ch:26831582017
spellingShingle Mathematical Physics and Mathematics
Chen, Ding-Geng
Chen, John
Monte-carlo simulation-based statistical modeling
title Monte-carlo simulation-based statistical modeling
title_full Monte-carlo simulation-based statistical modeling
title_fullStr Monte-carlo simulation-based statistical modeling
title_full_unstemmed Monte-carlo simulation-based statistical modeling
title_short Monte-carlo simulation-based statistical modeling
title_sort monte-carlo simulation-based statistical modeling
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-981-10-3307-0
http://cds.cern.ch/record/2683158
work_keys_str_mv AT chendinggeng montecarlosimulationbasedstatisticalmodeling
AT chenjohn montecarlosimulationbasedstatisticalmodeling