Statistical Modeling and Applications
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Beschreibung
In an era defined by the seamless integration of data and sophisticated analytical and modeling techniques, the quest for advanced statistical modeling and methodologies has never been more pertinent. Biostatistics Modeling and Public Health Applications: Study Design and Analysis Methodology in Health Sciences - Volume 1 has eleven chapters, divided in two Parts, with Part I comprising five chapters dealing with the application of Multivariate Analysis techniques and multivariate distributions to a set of different situations, and Part II consisting of six chapters which address the modeling of several interesting phenomena through the use of Heavy-Tailed, Skewed, Circular-Linear and Mixture Distributions, as well as Neural Networks. Statistical Modeling and Applications: Multivariate, Heavy-Tailed, Skewed Distributions, Mixture and Neural-Network Modeling, Volume 2, represents a concerted effort to bridge the gap between theoretical advancements and practical applications in the realm of Statistical Science, namely in the area of Statistical Modeling. It also aims to present a wide range of emerging topics in mathematical and statistical modeling written by a group of distinguished researchers from top-tier universities and research institutes to offer broader opportunities in stimulating further collaborations in the areas of mathematics and statistics.
Topics should appeal to both expert statisticians, as well as health researchers interested in biostatistical methodological applications in evidence-based health research. The book is a resourceful manual and can be used as an authoritative reference. The features covered in this book will appeal to researchers where public health research is being rigorously conducted.
In an era defined by the seamless integration of data and sophisticated analytical and modeling techniques, the quest for advanced statistical modeling and methodologies has never been more pertinent. Biostatistics Modeling and Public Health Applications: Study Design and Analysis Methodology in Health Sciences - Volume 1 has eleven chapters, divided in two Parts, with Part I comprising five chapters dealing with the application of Multivariate Analysis techniques and multivariate distributions to a set of different situations, and Part II consisting of six chapters which address the modeling of several interesting phenomena through the use of Heavy-Tailed, Skewed, Circular-Linear and Mixture Distributions, as well as Neural Networks. Statistical Modeling and Applications: Multivariate, Heavy-Tailed, Skewed Distributions, Mixture and Neural-Network Modeling, Volume 2, represents a concerted effort to bridge the gap between theoretical advancements and practical applications in the realm of Statistical Science, namely in the area of Statistical Modeling. It also aims to present a wide range of emerging topics in mathematical and statistical modeling written by a group of distinguished researchers from top-tier universities and research institutes to offer broader opportunities in stimulating further collaborations in the areas of mathematics and statistics.
Topics should appeal to both expert statisticians, as well as health researchers interested in biostatistical methodological applications in evidence-based health research. The book is a resourceful manual and can be used as an authoritative reference. The features covered in this book will appeal to researchers where public health research is being rigorously conducted.
Presents cutting-edge evidence-based public health research Includes biostatistical modeling with applications to real-world applications Provides theories and methods through their applications to evidence-based public health research and decision-making
Autor*in
Ding-Geng Chen
Themen in »Statistical Modeling and Applications«
Mathematical modelling statistical modelling spatial statistics Susceptible-Infected-Recovered (SIR) model maximum likelihood estimation causal inference survival analysis high-dimensional data Higher order asymptotics Hypothesis Testing Poly-cylindrical Data