Analysis of Step-Stress Models
Autor: | Debasis Kundu, Ayon Ganguly |
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EAN: | 9780081012406 |
eBook Format: | ePUB/PDF |
Sprache: | Englisch |
Produktart: | eBook |
Veröffentlichungsdatum: | 29.06.2017 |
Untertitel: | Existing Results and Some Recent Developments |
Kategorie: | |
Schlagworte: | Bayesian Life Testing experiment exponential distribution probability models |
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Analysis of Step-Stress Models: Existing Results and Some Recent Developments describes, in detail, the step-stress models and related topics that have received significant attention in the last few years. Although two books, Bagdonavicius and Nikulin (2001) and Nelson (1990), on general accelerated life testing models are available, no specific book is available on step-stress models. Due to the importance of this particular topic, Balakrishnan (2009) provided an excellent review for exponential step-stress models. The scope of this book is much more, providing the inferential issues for different probability models, both from the frequentist and Bayesian points-of-view. - Explains the different distributions of the Cumulative Exposure Mode - Covers many different models used for step-stress analysis - Discusses Step-stress life testing under the competing or complementary risk model
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.
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.