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Record Nr. |
UNINA9910150455503321 |
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Titolo |
Statistical Applications from Clinical Trials and Personalized Medicine to Finance and Business Analytics : Selected Papers from the 2015 ICSA/Graybill Applied Statistics Symposium, Colorado State University, Fort Collins / / edited by Jianchang Lin, Bushi Wang, Xiaowen Hu, Kun Chen, Ray Liu |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
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ISBN |
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Edizione |
[1st ed. 2016.] |
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Descrizione fisica |
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1 online resource (XV, 359 p. 68 illus., 44 illus. in color.) |
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Collana |
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ICSA Book Series in Statistics, , 2199-0999 |
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Disciplina |
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Soggetti |
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Biometry |
Statistics |
Computer science - Mathematics |
Mathematical statistics |
Biostatistics |
Statistical Theory and Methods |
Probability and Statistics in Computer Science |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters and index. |
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Sommario/riassunto |
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The papers in this volume represent a broad, applied swath of advanced contributions to the 2015 ICSA/Graybill Applied Statistics Symposium of the International Chinese Statistical Association, held at Colorado State University in Fort Collins. The contributions cover topics that range from statistical applications in business and finance to applications in clinical trials and biomarker analysis. Each papers was peer-reviewed by at least two referees and also by an editor. The conference was attended by over 400 participants from academia, industry, and government agencies around the world, including from North America, Asia, and Europe. Focuses on statistical applications from clinical trials, biomarker analysis, and personalized medicine to |
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applications in finance and business analytics A unique selection of papers from broad and multi-disciplinary critical hot topics - from academic, government, and industry perspectives - to appeal to a wide variety of applied research interests All papers feature original, peer-reviewed content. |
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