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9783030958633: Multivariate Statistical Analysis in the Real and Complex Domains

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This book serves as a practical resource for start-ups looking for innovating their business models in domestic and global markets. It describes the innovative business practices adopted by start-ups during the COVID-19 pandemic, with a special emphasis on value proposition innovation and business model innovation more generally. The BMI-Pandemic 2.15 model, which is an expanded version of the Odyssey 3.14 model, is presented to highlight 15 guidelines for innovating business models during pandemics. In order to promote open innovation, this book emphasizes the value of strategic alliances with academic libraries, peer start-ups, and freelancers. Additionally, using actual start-up case studies, it demonstrates how important technological innovation is for gathering feedback, prototyping, and conducting both secondary as well as primary market research. The need of regularly experimenting with new approaches, learning from mistakes, and enhancing current processes are also emphasized in this book. Theoretical insights are linked with practical experiences of start-ups amid the pandemic.

 

With a perfect balance of empirical research and assessment study types, this book is a source of quick knowledge for entrepreneurs, academics and researchers on how to enhance a company’s innovative capacities and success rates. 

This is an open access book.

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Informazioni sull?autore

A. M. Mathai is professor emeritus at McGill University and visiting professor at many other universities around the world. He has published over 37 books and over 300 research articles. Dr. Mathai has been invited to speak at conferences, universities, and other institutes around the world. His areas of research include applied statistics, probability, and mathematical statistics.


Serge B. Provost is professor at the University of Western Ontario. His research interests include multivariate analysis, computational statistics and distribution theory, with applications involving problems arising in various areas of scientific investigations such as biostatistics, finance, optics, imaging, and machine learning. Dr. Provost has received three teaching awards and chaired a national fellowship and scholarship selection committee. He is a fellow and chartered statistician of the Royal Statistical Society.

Hans J. Haubold is professor of theoretical astrophysics at the Office for Outer Space Affairs of the United Nations. His research interest focuses on the internal structure of the sun, solar neutrinos, and special functions of mathematical physics. He is also interested in the history of astronomy, physics, and mathematics, specifically Einstein's and Michelson's contributions to theoretical and experimental physics. Dr. Haubold is a member of the American Astronomical Society, the American Mathematical Society, and the History of Science Society.

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This book serves as a practical resource for start-ups looking for innovating their business models in domestic and global markets. It describes the innovative business practices adopted by start-ups during the COVID-19 pandemic, with a special emphasis on value proposition innovation and business model innovation more generally. The BMI-Pandemic 2.15 model, which is an expanded version of the Odyssey 3.14 model, is presented to highlight 15 guidelines for innovating business models during pandemics. In order to promote open innovation, this book emphasizes the value of strategic alliances with academic libraries, peer start-ups, and freelancers. Additionally, using actual start-up case studies, it demonstrates how important technological innovation is for gathering feedback, prototyping, and conducting both secondary as well as primary market research. The need of regularly experimenting with new approaches, learning from mistakes, and enhancing current processes are also emphasized in this book. Theoretical insights are linked with practical experiences of start-ups amid the pandemic.

 

With a perfect balance of empirical research and assessment study types, this book is a source of quick knowledge for entrepreneurs, academics and researchers on how to enhance a company’s innovative capacities and success rates.


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9783030958664: Multivariate Statistical Analysis in the Real and Complex Domains

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ISBN 10:  3030958663 ISBN 13:  9783030958664
Casa editrice: Springer Nature, 2023
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Arak M. Mathai
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book explores topics in multivariate statistical analysis, relevant in the real and complex domains. It utilizes simplified and unified notations to render the complex subject matter both accessible and enjoyable, drawing from clear exposition and numerous illustrative examples. The book features an in-depth treatment of theory with a fair balance of applied coverage, and a classroom lecture style so that the learning process feels organic. It also contains original results, with the goal of driving research conversations forward.This will be particularly useful for researchers working in machine learning, biomedical signal processing, and other fields that increasingly rely on complex random variables to model complex-valued data. It can also be used in advanced courses on multivariate analysis. Numerous exercises are included throughout. 952 pp. Englisch. Codice articolo 9783030958633

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Hardcover. Condizione: new. Hardcover. This book explores topics in multivariate statistical analysis, relevant in the real and complex domains. It utilizes simplified and unified notations to render the complex subject matter both accessible and enjoyable, drawing from clear exposition and numerous illustrative examples. The book features an in-depth treatment of theory with a fair balance of applied coverage, and a classroom lecture style so that the learning process feels organic. It also contains original results, with the goal of driving research conversations forward.This will be particularly useful for researchers working in machine learning, biomedical signal processing, and other fields that increasingly rely on complex random variables to model complex-valued data. It can also be used in advanced courses on multivariate analysis. Numerous exercises are included throughout. It also contains original results, with the goal of driving research conversations forward.This will be particularly useful for researchers working in machine learning, biomedical signal processing, and other fields that increasingly rely on complex random variables to model complex-valued data. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9783030958633

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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book explores topics in multivariate statistical analysis, relevant in the real and complex domains. It utilizes simplified and unified notations to render the complex subject matter both accessible and enjoyable, drawing from clear exposition and nume. Codice articolo 543920551

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Buch. Condizione: Neu. Neuware -This book explores topics in multivariate statistical analysis, relevant in the real and complex domains. It utilizes simplified and unified notations to render the complex subject matter both accessible and enjoyable, drawing from clear exposition and numerous illustrative examples. The book features an in-depth treatment of theory with a fair balance of applied coverage, and a classroom lecture style so that the learning process feels organic. It also contains original results, with the goal of driving research conversations forward.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 952 pp. Englisch. Codice articolo 9783030958633

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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book explores topics in multivariate statistical analysis, relevant in the real and complex domains. It utilizes simplified and unified notations to render the complex subject matter both accessible and enjoyable, drawing from clear exposition and numerous illustrative examples. The book features an in-depth treatment of theory with a fair balance of applied coverage, and a classroom lecture style so that the learning process feels organic. It also contains original results, with the goal of driving research conversations forward.This will be particularly useful for researchers working in machine learning, biomedical signal processing, and other fields that increasingly rely on complex random variables to model complex-valued data. It can also be used in advanced courses on multivariate analysis. Numerous exercises are included throughout. Codice articolo 9783030958633

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