Shrinkage Strategies In Analytics And Data Science
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Post Shrinkage Strategies In Statistical And Machine Learning For High Dimensional Data
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Author : Syed Ejaz Ahmed
language : en
Publisher: CRC Press
Release Date : 2023-05-25
Post Shrinkage Strategies In Statistical And Machine Learning For High Dimensional Data written by Syed Ejaz Ahmed and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-25 with Business & Economics categories.
This book presents some post-estimation and predictions strategies for the host of useful statistical models with applications in data science. It combines statistical learning and machine learning techniques in a unique and optimal way. It is well-known that machine learning methods are subject to many issues relating to bias, and consequently the mean squared error and prediction error may explode. For this reason, we suggest shrinkage strategies to control the bias by combining a submodel selected by a penalized method with a model with many features. Further, the suggested shrinkage methodology can be successfully implemented for high dimensional data analysis. Many researchers in statistics and medical sciences work with big data. They need to analyse this data through statistical modelling. Estimating the model parameters accurately is an important part of the data analysis. This book may be a repository for developing improve estimation strategies for statisticians. This book will help researchers and practitioners for their teaching and advanced research, and is an excellent textbook for advanced undergraduate and graduate courses involving shrinkage, statistical, and machine learning. The book succinctly reveals the bias inherited in machine learning method and successfully provides tools, tricks and tips to deal with the bias issue. Expertly sheds light on the fundamental reasoning for model selection and post estimation using shrinkage and related strategies. This presentation is fundamental, because shrinkage and other methods appropriate for model selection and estimation problems and there is a growing interest in this area to fill the gap between competitive strategies. Application of these strategies to real life data set from many walks of life. Analytical results are fully corroborated by numerical work and numerous worked examples are included in each chapter with numerous graphs for data visualization. The presentation and style of the book clearly makes it accessible to a broad audience. It offers rich, concise expositions of each strategy and clearly describes how to use each estimation strategy for the problem at hand. This book emphasizes that statistics/statisticians can play a dominant role in solving Big Data problems, and will put them on the precipice of scientific discovery. The book contributes novel methodologies for HDDA and will open a door for continued research in this hot area. The practical impact of the proposed work stems from wide applications. The developed computational packages will aid in analyzing a broad range of applications in many walks of life.
Proceedings Of The Thirteenth International Conference On Management Science And Engineering Management
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Author : Jiuping Xu
language : en
Publisher: Springer
Release Date : 2019-06-19
Proceedings Of The Thirteenth International Conference On Management Science And Engineering Management written by Jiuping Xu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-19 with Computers categories.
This book gathers the proceedings of the 13th International Conference on Management Science and Engineering Management (ICMSEM 2019), which was held at Brock University, Ontario, Canada on August 5–8, 2019. Exploring the latest ideas and pioneering research achievements in management science and engineering management, the respective contributions highlight both theoretical and practical studies on management science and computing methodologies, and present advanced management concepts and computing technologies for decision-making problems involving large, uncertain and unstructured data. Accordingly, the proceedings offer researchers and practitioners in related fields an essential update, as well as a source of new research directions.
Big Data Analytics Strategies For The Smart Grid
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Author : Carol L. Stimmel
language : en
Publisher: CRC Press
Release Date : 2016-04-19
Big Data Analytics Strategies For The Smart Grid written by Carol L. Stimmel and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-19 with Computers categories.
A comprehensive data analytics program is the only way utilities will be able to meet the challenges of modern grids with operational efficiency, while reconciling the demands of greenhouse gas legislation, and establishing a meaningful return on investment from smart grid deployments. This book addresses the requirements for applying big data technologies and approaches, including Big Data cybersecurity, to the critical infrastructure that makes up the electrical utility grid.
Financial Statistics And Data Analytics
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Author : Shuangzhe Li
language : en
Publisher: MDPI
Release Date : 2021-03-02
Financial Statistics And Data Analytics written by Shuangzhe Li and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-02 with Business & Economics categories.
Modern financial management is largely about risk management, which is increasingly data-driven. The problem is how to extract information from the data overload. It is here that advanced statistical and machine learning techniques can help. Accordingly, finance, statistics, and data analytics go hand in hand. The purpose of this book is to bring the state-of-art research in these three areas to the fore and especially research that juxtaposes these three.
Supporting Shrinkage
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Author : Michael P. Johnson
language : en
Publisher: State University of New York Press
Release Date : 2021-08-01
Supporting Shrinkage written by Michael P. Johnson and has been published by State University of New York Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-01 with Political Science categories.
Supporting Shrinkage describes a new approach to citizen-engaged, community-focused planning methods and technologies for cities and regions facing decline, disinvestment, shrinkage, and social and physical distress. The volume evaluates the benefits and costs of a wide range of analytic approaches for designing policy and planning interventions for shrinking cities and distressed communities. These include collaborative planning, social media, civic technology, game design, analytics, decision modeling and decision support, and spatial analysis. The authors present case studies of three US cities addressing shrinkage and decline, with a focus on issues of social justice, democratization of knowledge, and local empowerment. Proposed as a solution is an approach that puts community engagement and empowerment at the center, combined with data and technology innovations. The authors argue that decisions informed by qualitative and quantitative data and analytic methods, implemented through accessible and affordable technologies, and based on notions of social impact and social justice, can enable residents to play a leading role in the positive transformation of shrinking cities and distressed communities.
Proceedings Of The Fourteenth International Conference On Management Science And Engineering Management
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Author : Jiuping Xu
language : en
Publisher: Springer Nature
Release Date : 2020-06-22
Proceedings Of The Fourteenth International Conference On Management Science And Engineering Management written by Jiuping Xu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-22 with Technology & Engineering categories.
This book gathers the proceedings of the 14th International Conference on Management Science and Engineering Management (ICMSEM 2020). Held at the Academy of Studies of Moldova from July 30 to August 2, 2020, the conference provided a platform for researchers and practitioners in the field to share their ideas and experiences. Covering a wide range of topics, including hot management issues in engineering science, the book presents novel ideas and the latest research advances in the area of management science and engineering management. It includes both theoretical and practical studies of management science applied in computing methodology, highlighting advanced management concepts, and computing technologies for decision-making problems involving large, uncertain and unstructured data. The book also describes the changes and challenges relating to decision-making procedures at the dawn of the big data era, and discusses new technologies for analysis, capture, search, sharing, storage, transfer and visualization, and in the context of privacy violations, as well as advances in the integration of optimization, statistics and data mining. Given its scope, it will appeal to a wide readership, particularly those looking for new ideas and research directions.
Data Science And Data Analytics
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Author : Amit Kumar Tyagi
language : en
Publisher: CRC Press
Release Date : 2021-09-22
Data Science And Data Analytics written by Amit Kumar Tyagi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-22 with Computers categories.
Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured (labeled) and unstructured (unlabeled) data. It is the future of Artificial Intelligence (AI) and a necessity of the future to make things easier and more productive. In simple terms, data science is the discovery of data or uncovering hidden patterns (such as complex behaviors, trends, and inferences) from data. Moreover, Big Data analytics/data analytics are the analysis mechanisms used in data science by data scientists. Several tools, such as Hadoop, R, etc., are used to analyze this large amount of data to predict valuable information and for decision-making. Note that structured data can be easily analyzed by efficient (available) business intelligence tools, while most of the data (80% of data by 2020) is in an unstructured form that requires advanced analytics tools. But while analyzing this data, we face several concerns, such as complexity, scalability, privacy leaks, and trust issues. Data science helps us to extract meaningful information or insights from unstructured or complex or large amounts of data (available or stored virtually in the cloud). Data Science and Data Analytics: Opportunities and Challenges covers all possible areas, applications with arising serious concerns, and challenges in this emerging field in detail with a comparative analysis/taxonomy. FEATURES Gives the concept of data science, tools, and algorithms that exist for many useful applications Provides many challenges and opportunities in data science and data analytics that help researchers to identify research gaps or problems Identifies many areas and uses of data science in the smart era Applies data science to agriculture, healthcare, graph mining, education, security, etc. Academicians, data scientists, and stockbrokers from industry/business will find this book useful for designing optimal strategies to enhance their firm’s productivity.
Big Data And Information Theory
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Author : Jiuping Xu
language : en
Publisher: Routledge
Release Date : 2022-06-01
Big Data And Information Theory written by Jiuping Xu and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Business & Economics categories.
Big Data and Information Theory are a binding force between various areas of knowledge that allow for societal advancement. Rapid development of data analytic and information theory allows companies to store vast amounts of information about production, inventory, service, and consumer activities. More powerful CPUs and cloud computing make it possible to do complex optimization instead of using heuristic algorithms, as well as instant rather than offline decision-making. The era of "big data" challenges includes analysis, capture, curation, search, sharing, storage, transfer, visualization, and privacy violations. Big data calls for better integration of optimization, statistics, and data mining. In response to these challenges this book brings together leading researchers and engineers to exchange and share their experiences and research results about big data and information theory applications in various areas. This book covers a broad range of topics including statistics, data mining, data warehouse implementation, engineering management in large-scale infrastructure systems, data-driven sustainable supply chain network, information technology service offshoring project issues, online rumors governance, preliminary cost estimation, and information system project selection. The chapters in this book were originally published in the journal, International Journal of Management Science and Engineering Management.
Data Science And Predictive Analytics
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Author : Ivo D. Dinov
language : en
Publisher: Springer Nature
Release Date : 2023-02-16
Data Science And Predictive Analytics written by Ivo D. Dinov and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-16 with Computers categories.
This textbook integrates important mathematical foundations, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and decision science challenges. Each concept in the book includes a rigorous symbolic formulation coupled with computational algorithms and complete end-to-end pipeline protocols implemented as functional R electronic markdown notebooks. These workflows support active learning and demonstrate comprehensive data manipulations, interactive visualizations, and sophisticated analytics. The content includes open problems, state-of-the-art scientific knowledge, ethical integration of heterogeneous scientific tools, and procedures for systematic validation and dissemination of reproducible research findings. Complementary to the enormous challenges related to handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there are unique opportunities that come with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics covered in Data Science and Predictive Analytics address specific knowledge gaps, resolve educational barriers, and mitigate workforce information-readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical principles, modern computational methods, advanced data science techniques, model-based machine learning, model-free artificial intelligence, and innovative biomedical applications. The book’s fourteen chapters start with an introduction and progressively build foundational skills from visualization to linear modeling, dimensionality reduction, supervised classification, black-box machine learning techniques, qualitative learning methods, unsupervised clustering, model performance assessment, feature selection strategies, longitudinal data analytics, optimization, neural networks, and deep learning. The second edition of the book includes additional learning-based strategies utilizing generative adversarial networks, transfer learning, and synthetic data generation, as well as eight complementary electronic appendices. This textbook is suitable for formal didactic instructor-guided course education, as well as for individual or team-supported self-learning. The material is presented at the upper-division and graduate-level college courses and covers applied and interdisciplinary mathematics, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical sciences. The analytical techniques and predictive scientific methods described in the book may be useful to a wide range of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory, funding, and policy agencies. The supporting book website provides many examples, datasets, functional scripts, complete electronic notebooks, extensive appendices, and additional materials.
Strategic Analytics
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Author : Martin Kunc
language : en
Publisher: John Wiley & Sons
Release Date : 2018-11-13
Strategic Analytics written by Martin Kunc and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-13 with Business & Economics categories.
Defines common ground at the interface of strategy and management science and unites the topics with an original approach vital for strategy students, researchers and managers Strategic Analytics: Integrating Management Science and Strategy combines strategy content with strategy process through the lenses of management science, masterfully defining the common ground that unites both fields. Each chapter starts with the perspective of a certain strategy problem, such as competition, but continues with an explanation of the strategy process using management science tools such as simulation. Facilitating the process of strategic decision making through the lens of management science, the author integrates topics that are usually in conflict for MBAs: strategy and quantitative methods. Strategic Analytics features multiple international real-life case studies and examples, business issues for further research and theory review questions and exercises at the end of each chapter. Strategic Analytics starts by introducing readers to strategic management. It then goes on to cover: managerial capabilities for a complex world; politics, economy, society, technology, and environment; external environments known as exogenous factors (PESTE) and endogenous factors (industry); industry dynamics; industry evolution; competitive advantage; dynamic resource management; organisational design; performance measurement system; the life cycle of organisations from start-ups; maturity for maintaining profitability and growth; and finally, regeneration. Developed from the author's own Strategy Analytics course at Warwick Business School, personal experience as consultant, and in consultation with other leading scholars Uses management science to facilitate the process of strategic decision making Chapters structured with chapter objectives, summaries, short case studies, tables, student exercises, references and management science models Accompanied by a supporting website Aimed at both academics and practitioners, Strategic Analytics is an ideal text for postgraduates and advanced undergraduate students of business and management.