Hybrid Censoring Know-How

Designs and Implementations
Erscheinungsjahr 2023
ISBN: 978-0-12-398387-9
Verlag: William Andrew Publishing

Buch, Englisch, 320 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 1000 g

Hybrid Censoring Know-How: Models, Methods and Applications focuses on hybrid censoring, an important topic in censoring methodology with numerous applications. The readers will find information on the significance of censored data in theoretical and applied contexts, and descriptions of extensive data sets from life-testing experiments where these forms of data naturally occur. The existing literature on censoring methodology, life-testing procedures, and lifetime data analysis provides only hybrid censoring schemes, with little information about hybrid censoring methodologies, ideas, and statistical inferential methods. This book fills that gap, featuring statistical tools applicable to data from medicine, biology, public health, epidemiology, engineering, economics, and demography.

Zielgruppe

Life science and engineering scientists and researchers who need to analyze censored or truncated lifetime data and students, researchers and practitioners in different areas such as statistics, industrial engineering and clinical trials.

Autoren/Hrsg.

Balakrishnan, Narayanaswamy

Cramer, Erhard

Kundu, Debasis

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure

Inhaltsverzeichnis

1. Introduction2. Preliminaries3. Inference for Type-II, Type-I, and progressive censoring4. Models and distributional properties of hybrid censoring designs5. Inference for exponentially distributed lifetimes6. Inference for other lifetime distributions7. Progressive hybrid censored data8. Informationmeasures9. Step-stress testing10. Applications in reliability11. Goodness-of-fit tests12. Prediction methods13. Adaptive progressive hybrid censoringAppendix

Über Autor(innen)

Balakrishnan, Narayanaswamy
Narayanaswamy Balakrishnan is a distinguished university professor in the Department of Mathematics and Statistics at McMaster University Hamilton, Ontario, Canada. He is an internationally recognized expert on statistical distribution theory, and a book-powerhouse with over 24 authored books, four authored handbooks, and 30 edited books under his name. He is currently the Editor-in-Chief of Communications in Statistics published by Taylor & Francis. He was also the Editor-in-Chief for the revised version of Encyclopedia of Statistical Sciences published by John Wiley & Sons. He is a Fellow of the American Statistical Association and a Fellow of the Institute of Mathematical Statistics. In 2016, he was awarded an Honorary Doctorate from The National and Kapodistrian University of Athens, Athens, Greece. In 2021, he was elected as a Fellow of the Royal Society of Canada.

Cramer, Erhard
Erhard Cramer is a Professor in the Institute for Statistics at RWTH Aachen University in Aachen, Germany. He has numerous publications to his credit and his research interests include order statistics, generalized order statistics, censoring methodology, B-spline theory, and statistical inference. He is a coauthor of the book The Art of Progressive Censoring: Applications to Reliability and Quality published by Birkhäuser, Boston, in 2014.

Kundu, Debasis
Debasis Kundu is a Professor in the Department of Mathematics and Statistics at the Indian Institute of Technology Kanpur, India, which he joined in 1990. He had previously worked as Assistant Professor at the University of Texas at Dallas, USA, after completing his PhD in Statistics at Pennsylvania State University, USA. His research interests include statistical signal processing, nonlinear regression, distribution theory, statistical computing, and reliability and survival analysis.