H J Vaman, Prabhanjan Tattar

Survival Analysis (eBook, ePUB)

• Format: ePub

Produktbeschreibung

The purpose of this book is to provide an account of survival analysis. The authors intend to accomplish it from two fronts: (i) methods in survival analysis developed over the past two decades and extending the scope of existing body of methods, and (ii) augmenting the traditional methods with their counterpart in machine learning.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 296
• Erscheinungstermin: 26. August 2022
• Englisch
• ISBN-13: 9781000624076
• Artikelnr.: 64285962

Autorenporträt

Prabhanjan Narayanachar Tattar is working as a Lead Data Scientist at British American Tobacco company, Malaysia. The author has published several books in Statistics: A Course in Statistics with R (Wiley), Statistical Application Development with R and Python, and Hands-on Ensemble Learning with R. He is recipient of the IBS(IR)- GK Shukla Young Biometrician Award (2005) and the Dr. U.S. Nair Award for Young Statistician (2007). He held SRF of CSIR-UGC during PhD. In the year 2021, he has ventured into ction writing and published three novels under the penname of S.B. Akshobhya: The Panipuri Crimes, Finding - A Measure of Her, and Prema Naada Pandita.

H. J. Vaman is a retired professor of Statistics. He taught for over 40 years at Bangaore University and Central University of Rajasthan. He has also served as visiting faculty at Shivaji University, University of Calcutta, Indian Statistical Institute, Bangalore Centre, IIT-Mumbai, and Mangalore University. His main areas of research are sequential decision processes, survival analysis, statistical process control, and modelling in certain health related studies.

Inhaltsangabe

I Classical Survival Analysis. 1. Lifetime Data and Concepts. 2 Core Concepts. 3. Inference - Estimation. 4. Inference - Statistical Tests. 5. Regression Models. 6. Further Topics in Regression Models. 7. Model Selection.

II Machine Learning Methods. Why Machine Learning? 8. Survival Trees. 9. Ensemble Survival Analysis. 10. Neural Network Survival Analysis. 11. Complementary Machine Learning Techniques. Bibliography. Index.