Kernel Ridge Regression in Clinical Research - Rilegato

Cleophas, Ton J.; Zwinderman, Aeilko H.

Rilegato

ISBN 10: 3031107160 ISBN 13: 9783031107160

Casa editrice: Springer Nature, 2022

Riassunto

IBM (international business machines) has published in its SPSS statistical software 2022 update a very important novel regression method entitled Kernel Ridge Regression (KRR). It is an extension of the currently available regression methods, and is suitable for pattern recognition in high dimensional data, particularly, when alternative methods fail. Its theoretical advantages are plenty and include the

• kernel trick for reduced arithmetic complexity,
• estimation of uncertainty by Gaussians unlike histograms,
• corrected data-overfit by ridge regularization,
• availability of 8 alternative kernel density models for datafit.

A very exciting and wide array of preliminary KRR research has already been published by major disciplines (like studies in quantum mechanics and nuclear physics, studies of molecular affinity / dynamics, atomisation energy studies, but also forecasting economics studies, IoT (internet of things) studies for e-networks, plant stress response studies, big data streaming studies, etc). In contrast, it is virtually unused in clinical research. This edition is the first textbook and tutorial of kernel ridge regressions for medical and healthcare students as well as recollection / update bench, and help desk for professionals. Each chapter can be studied as a standalone, and, using, real as well as hypothesized data, it tests the performance of the novel methodology against traditional regression analyses. Step by step analyses of over 20 data files stored at Supplementary Files at Springer Interlink are included for self-assessment. We should add that the authors are well qualified in their field. Professor Zwinderman is past-president of the International Society of Biostatistics (2012-2015) and Professor Cleophas is past-president of the American College of Angiology (2000-2002). From their expertise they should be able to make adequate selections of modern KRR methods for the benefit of physicians, students, and investigators. The authors have been working and publishing together for 24 years and their research can be characterized as a continued effort to demonstrate that clinical data analysis is not mathematics but rather a discipline at the interface of biology and mathematics.

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Informazioni sull?autore

Professor Dr. T.J. Cleophas is internist / clinical pharmacologist / statistician at the educational Albert Schweitzer Hospital Dordrecht Netherlands. He is the writer of many statistics textbooks, and he tutors statistics at the Universities of Amsterdam, Rotterdam, Utrecht, Maastricht, Leiden, Nijmegen, Netherlands. In 2020-2022 he was the invited author and editor of Springer Heidelberg Series on Machine Learning and Statistics Applied to Clinical Studies, which were bought by over 30 million professionals involved in Coronavirus research. He is currently completing an edition entitled "Kernel Ridge Regression in Clinical Research", addressing a novel methodology for big and multidimensiomal data analysis.

Professor Dr. A.H. Zwinderman is mathematical PhD, full professor of statistics, and principal investigator at the Academic Medical Center, University of Amsterdam. He authored 663 scientific papers and developed many novel statistical methods with particular focus on omics and big data research. He is co-founder of the sparse canonical methodology for the analysis of data with thousands of predictor variables, and together with Professor Cleophas he contributed to the statistical methods series in the journal Circulation. Much of his current work involves studies based on methodologies like parallel computing, the use of clustercomputers, GPU computing, and grid computing. 

Dalla quarta di copertina

IBM (international business machines) has published in its SPSS statistical software 2022 update a very important novel regression method entitled Kernel Ridge Regression (KRR). It is an extension of the currently available regression methods, and is suitable for pattern recognition in high dimensional data, particularly, when alternative methods fail. Its theoretical advantages are plenty and include the

• kernel trick for reduced arithmetic complexity,
• estimation of uncertainty by Gaussians unlike histograms,
• corrected data-overfit by ridge regularization,
• availability of 8 alternative kernel density models for datafit.

A very exciting and wide array of preliminary KRR research has already been published by major disciplines (like studies in quantum mechanics and nuclear physics, studies of molecular affinity / dynamics, atomisation energy studies, but also forecasting economics studies, IoT (internet of things)studies for e-networks, plant stress response studies, big data streaming studies, etc). In contrast, it is virtually unused in clinical research. This edition is the first textbook and tutorial of kernel ridge regressions for medical and healthcare students as well as recollection / update bench, and help desk for professionals. Each chapter can be studied as a standalone, and, using, real as well as hypothesized data, it tests the performance of the novel methodology against traditional regression analyses. Step by step analyses of over 20 data files stored at Supplementary Files at Springer Interlink are included for self-assessment. We should add that the authors are well qualified in their field. Professor Zwinderman is past-president of the International Society of Biostatistics (2012-2015) and Professor Cleophas is past-president of the American College of Angiology (2000-2002). From their expertise they should be able to make adequate selections of modern KRR methods for the benefit of physicians, students, and investigators. The authors have been working and publishing together for 24 years and their research can be characterized as a continued effort to demonstrate that clinical data analysis is not mathematics but rather a discipline at the interface of biology and mathematics.

Dettagli dell?edizione

• Editore: Springer Nature
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 3031107160
• ISBN 13: 9783031107160
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 308
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania