Overview
- Discusses the use of modern statistical methods for the estimation of the performance measures of reliability systems that operate under different conditions
- Presents some numerical or simulation methods as illustrative examples where computer-based methods are implemented
- Selects a concrete modelling scheme for each practical situation and conducts a nonparametric inference procedure
- Includes supplementary material: sn.pub/extras
Part of the book series: Springer Series in Reliability Engineering (RELIABILITY)
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Table of contents (7 chapters)
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Front Matter
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Black-Box Approach: Time-to-Failure Analysis
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Front Matter
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Grey-Box Approach: Counting Processes
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Front Matter
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White-Box Approach: The Physical Environment
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Front Matter
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Back Matter
Keywords
About this book
Nonparametric statistics has probably become the leading methodology for researchers performing data analysis. It is nevertheless true that, whereas these methods have already proved highly effective in other applied areas of knowledge such as biostatistics or social sciences, nonparametric analyses in reliability currently form an interesting area of study that has not yet been fully explored.
Applied Nonparametric Statistics in Reliability is focused on the use of modern statistical methods for the estimation of dependability measures of reliability systems that operate under different conditions. The scope of the book includes:
- smooth estimation of the reliability function and hazard rate of non-repairable systems;
- study of stochastic processes for modelling the time evolution of systems when imperfect repairs are performed;
- nonparametric analysis of discrete and continuous time semi-Markov processes;
- isotonic regression analysis of the structure function of a reliability system, and
- lifetime regression analysis.
Besides the explanation of the mathematical background, several numerical computations or simulations are presented as illustrative examples. The corresponding computer-based methods have been implemented using R and MATLAB®. A concrete modelling scheme is chosen for each practical situation and, in consequence, a nonparametric inference procedure is conducted.
Applied Nonparametric Statistics in Reliability will serve the practical needs of scientists (statisticians and engineers) working on applied reliability subjects.
Authors and Affiliations
About the authors
M. Luz Gámiz is an associate professor in the Department of Statistics and Operational Research at the University of Granada, Granada, Spain.
K. B. Kulasekera is a professor and graduate program coordinator in the Department of Mathematical Sciences at Clemson University, Clemson, USA.
Nikolaos Limnios is a professor at the Université de Technologie de Compiègne, Compiègne, France.
Bo Henry Lindqvist is a professor of statistics in the Department of Mathematical Sciences at the Norwegian University of Science and Technology, Trondheim, Norway.
Bibliographic Information
Book Title: Applied Nonparametric Statistics in Reliability
Authors: M. Luz Gámiz, K. B. Kulasekera, Nikolaos Limnios, Bo Henry Lindqvist
Series Title: Springer Series in Reliability Engineering
DOI: https://doi.org/10.1007/978-0-85729-118-9
Publisher: Springer London
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag London Limited 2011
Hardcover ISBN: 978-0-85729-117-2Published: 23 February 2011
Softcover ISBN: 978-1-4471-2634-8Published: 11 October 2014
eBook ISBN: 978-0-85729-118-9Published: 14 February 2011
Series ISSN: 1614-7839
Series E-ISSN: 2196-999X
Edition Number: 1
Number of Pages: XIII, 230
Topics: Quality Control, Reliability, Safety and Risk, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences