Yichuan Zhao – författare

Advanced Statistical Methods for Transportation Economics and Engineering presents recent advances and contemporary approaches in statistics, machine learning, and computational modeling, catered to the evolving demands of transportation systems. It analyses and interrogates smart statistical tools — like machine learning, time-series analysis, and spatial statistics — to help make sense of complex and heterogeneous transport systems.The fields of transportation economics and engineering are undergoing rapid transformation, driven by advancements in transportation systems, emerging technologies, and the imperative for sustainable solutions. To navigate these changes effectively, professionals and researchers require robust statistical tools to analyze the complex interplay of the economic, social, and engineering factors that are shaping modern transportation. Bridging the gap between theory and practice, this book emphasizes innovative approaches, interdisciplinary perspectives, and real-world applications. Emphasis is placed on interpretability and actionable results, ensuring that technical developments translate into practical improvements in transport systems.With contributions from a range of leading interdisciplinary experts and a plethora of real-world case studies, academics, practitioners, and policymakers in transport, sustainability, logistics, and urban planning will find within these pages a crucial resource for applying advanced analytical techniques to the numerous economic and engineering challenges shaping the future of transportation.

This edited collection discusses the emerging topics in statistical modeling for biomedical research. Leading experts in the frontiers of biostatistics and biomedical research discuss the statistical procedures, useful methods, and their novel applications in biostatistics research.

This edited collection discusses the emerging topics in statistical modeling for biomedical research. Leading experts in the frontiers of biostatistics and biomedical research discuss the statistical procedures, useful methods, and their novel applications in biostatistics research.

This book brings together the voices of leading experts in the frontiers of biostatistics, biomedicine, and the health sciences to discuss the statistical procedures, useful methods, and novel applications in biostatistics research. It also includes discussions of potential future directions of biomedicine and new statistical developments for health research, with the intent of stimulating research and fostering the interactions of scholars across health research related disciplines. Topics covered include:Health data analysis and applications to EHR dataClinical trials, FDR, and applications in health scienceBig network analytics and its applications in GWASSurvival analysis and functional data analysisGraphical modelling in genomic studiesThe book will be valuable to data scientists and statisticians who are working in biomedicine and health, other practitioners in the health sciences, and graduate students and researchers in biostatistics and health.

This book brings together the voices of leading experts in the frontiers of biostatistics, biomedicine, and the health sciences to discuss the statistical procedures, useful methods, and novel applications in biostatistics research. It also includes discussions of potential future directions of biomedicine and new statistical developments for health research, with the intent of stimulating research and fostering the interactions of scholars across health research related disciplines. Topics covered include:Health data analysis and applications to EHR dataClinical trials, FDR, and applications in health scienceBig network analytics and its applications in GWASSurvival analysis and functional data analysisGraphical modelling in genomic studiesThe book will be valuable to data scientists and statisticians who are working in biomedicine and health, other practitioners in the health sciences, and graduate students and researchers in biostatistics and health.

This book is a practical guide on the use of various data analytics and visualization techniques and tools in the banking and financial sectors. It focuses on how combining expertise from interdisciplinary areas, such as machine learning and business analytics, can bring forward a shared vision on the benefits of data science from the research point of view to the evaluation of policies. It highlights how data science is reshaping the business sector. It includes examples of novel big data sources and some successful applications on the use of advanced machine learning, natural language processing, networks analysis, and time series analysis and forecasting, among others, in the banking and finance.It includes several case studies where innovative data science models is used to analyse, test or model some crucial phenomena in banking and finance. At the same time, the book is making an appeal for a further adoption of these novel applications in the field of economics and finance so that they can reach their full potential and support policy-makers and the related stakeholders in the transformational recovery of our societies.The book is for stakeholders involved in research and innovation in the banking and financial sectors, but also those in the fields of computing, IT and managerial information systems, helping through this new theory to better specify the new opportunities and challenges. The many real cases addressed in this book also provide a detailed guide allowing the reader to realize the latest methodological discoveries and the use of the different Machine Learning approaches (supervised, unsupervised, reinforcement, deep, etc.) and to learn how to use and evaluate performance of new data science tools and frameworks

This book is a practical guide on the use of various data analytics and visualization techniques and tools in the banking and financial sectors. It focuses on how combining expertise from interdisciplinary areas, such as machine learning and business analytics, can bring forward a shared vision on the benefits of data science from the research point of view to the evaluation of policies. It highlights how data science is reshaping the business sector. It includes examples of novel big data sources and some successful applications on the use of advanced machine learning, natural language processing, networks analysis, and time series analysis and forecasting, among others, in the banking and finance.It includes several case studies where innovative data science models is used to analyse, test or model some crucial phenomena in banking and finance. At the same time, the book is making an appeal for a further adoption of these novel applications in the field of economics and finance so that they can reach their full potential and support policy-makers and the related stakeholders in the transformational recovery of our societies.The book is for stakeholders involved in research and innovation in the banking and financial sectors, but also those in the fields of computing, IT and managerial information systems, helping through this new theory to better specify the new opportunities and challenges. The many real cases addressed in this book also provide a detailed guide allowing the reader to realize the latest methodological discoveries and the use of the different Machine Learning approaches (supervised, unsupervised, reinforcement, deep, etc.) and to learn how to use and evaluate performance of new data science tools and frameworks

Particular attention is devoted to the interplay between precision health, big data, and mobile health research, while also exploring precision medicine's role in clinical trials, electronic health record data analysis, survival analysis, and genomic studies.

This book discusses statistical methods and their innovative applications in precision health. It serves as a valuable resource to foster the development of this growing field within the context of the big data era.  The chapters cover a wide range of topics, including foundational principles, statistical theories, new procedures, advanced methods, and practical applications in precision medicine. Particular attention is devoted to the interplay between precision health, big data, and mobile health research, while also exploring precision medicine's role in clinical trials, electronic health record data analysis, survival analysis, and genomic studies. Targeted at data scientists, statisticians, graduate students, and researchers in academia, industry, and government, this book offers insights into the latest advances in personalized medicine using advanced statistical techniques.

This book focuses on big data analytics in biostatistics and bioinformatics. As a contributed volume, it seeks to stimulate the growth of this dynamic field in the era of artificial intelligence. By presenting an overview of recent advances in biostatistics and bioinformatics through advanced statistical and machine learning methods, the book offers valuable insights for data scientists and biostatisticians working in public health, neuroscience, and related disciplines.It is a useful reference for graduate students and researchers across academia, industry, and government. This work is partially supported by the South African National Research Foundation (NRF) and South African Medical Research Council (SAMRC) (South African DST-NRF-SAMRC SARChI Research Chair in Biostatistics, Grant Number 114613).

This book is comprised of the presentations delivered at the 25th ICSA Applied Statistics Symposium held at the Hyatt Regency Atlanta, on June 12-15, 2016. This symposium attracted more than 700 statisticians and data scientists working in academia, government, and industry from all over the world. The theme of this conference was the “Challenge of Big Data and Applications of Statistics,” in recognition of the advent of big data era, and the symposium offered opportunities for learning, receiving inspirations from old research ideas and for developing new ones, and for promoting further research collaborations in the data sciences. The invited contributions addressed rich topics closely related to big data analysis in the data sciences, reflecting recent advances and major challenges in statistics, business statistics, and biostatistics. Subsequently, the six editors selected 19 high-quality presentations and invited the speakers to prepare full chapters for this book, which showcases new methods in statistics and data sciences, emerging theories, and case applications from statistics, data science and interdisciplinary fields. The topics covered in the book are timely and have great impact on data sciences, identifying important directions for future research, promoting advanced statistical methods in big data science, and facilitating future collaborations across disciplines and between theory and practice.

The theme of this conference was the “Challenge of Big Data and Applications of Statistics,” in recognition of the advent of big data era, and the symposium offered opportunities for learning, receiving inspirations from old research ideas and for developing new ones, and for promoting further research collaborations in the data sciences.