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Multivariate statistical analysis in...

Mathai, A. M.

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Multivariate statistical analysis in the real and complex domains

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multivariate statistical analysis in the real and complex domains/ by Arak M. Mathai, Serge B. Provost, Hans J. Haubold.
作者:	Mathai, A. M.
其他作者:	Provost, Serge B.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	xxii, 912 p. :ill. (some col.), digital ;24 cm.
內容註:	1. Mathematical Preliminaries -- 2. The Univariate Gaussian and Related Distribution -- 3. Multivariate Gaussian and Related Distributions -- 4. The Matrix-variate Gaussian Distribution -- 5. Matrix-variate Gamma and Beta Distributions -- 6. Hypothesis Testing and Null Distributions -- 7. Rectangular Matrix-variate Distributions -- 8. Distributions of Eigenvalues and Eigenvectors -- 9. Principal Component Analysis -- 10. Canonical Correlation Analysis -- 11. Factor Analysis -- 12. Classification Problems -- 13. Multivariate Analysis of Variance (MANOVA) -- 14. Profile Analysis and Growth Curves -- 15. Cluster Analysis and Correspondence Analysis.
Contained By:	Springer Nature eBook
標題:	Multivariate analysis. -
電子資源:	https://doi.org/10.1007/978-3-030-95864-0
ISBN:	9783030958640

Multivariate statistical analysis in the real and complex domains
Mathai, A. M.

Multivariate statistical analysis in the real and complex domains[electronic resource] /by Arak M. Mathai, Serge B. Provost, Hans J. Haubold. - Cham :Springer International Publishing :2022. - xxii, 912 p. :ill. (some col.), digital ;24 cm.

1. Mathematical Preliminaries -- 2. The Univariate Gaussian and Related Distribution -- 3. Multivariate Gaussian and Related Distributions -- 4. The Matrix-variate Gaussian Distribution -- 5. Matrix-variate Gamma and Beta Distributions -- 6. Hypothesis Testing and Null Distributions -- 7. Rectangular Matrix-variate Distributions -- 8. Distributions of Eigenvalues and Eigenvectors -- 9. Principal Component Analysis -- 10. Canonical Correlation Analysis -- 11. Factor Analysis -- 12. Classification Problems -- 13. Multivariate Analysis of Variance (MANOVA) -- 14. Profile Analysis and Growth Curves -- 15. Cluster Analysis and Correspondence Analysis.

Open Access

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.

ISBN: 9783030958640

Standard No.: 10.1007/978-3-030-95864-0doiSubjects--Topical Terms:

517467
Multivariate analysis.

LC Class. No.: QA278 / .M37 2022

Dewey Class. No.: 519.535

Multivariate statistical analysis in the real and complex domains
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505 0 $a 1. Mathematical Preliminaries -- 2. The Univariate Gaussian and Related Distribution -- 3. Multivariate Gaussian and Related Distributions -- 4. The Matrix-variate Gaussian Distribution -- 5. Matrix-variate Gamma and Beta Distributions -- 6. Hypothesis Testing and Null Distributions -- 7. Rectangular Matrix-variate Distributions -- 8. Distributions of Eigenvalues and Eigenvectors -- 9. Principal Component Analysis -- 10. Canonical Correlation Analysis -- 11. Factor Analysis -- 12. Classification Problems -- 13. Multivariate Analysis of Variance (MANOVA) -- 14. Profile Analysis and Growth Curves -- 15. Cluster Analysis and Correspondence Analysis.
506 0 $a Open Access
520 $a 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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