Applied Nonparametric Statistics in Reliability [electronic resource] / by M. Luz Gámiz, K. B. Kulasekera, Nikolaos Limnios, Bo Henry Lindqvist.

By: Gámiz, M. Luz [author.]Contributor(s): Kulasekera, K. B [author.] | Limnios, Nikolaos [author.] | Lindqvist, Bo Henry [author.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Springer Series in Reliability Engineering: Publisher: London : Springer London, 2011Description: XIII, 230p. 41 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9780857291189Subject(s): Engineering | System safety | Engineering | Quality Control, Reliability, Safety and Risk | Statistics for Engineering, Physics, Computer Science, Chemistry and Earth SciencesAdditional physical formats: Printed edition:: No titleDDC classification: 658.56 LOC classification: TA169.7T55-T55.3TA403.6Online resources: Click here to access online
Contents:
1. Lifetime Data -- 2. Models for Perfect Repair -- 3. Models for Minimal Repair -- 4. Models for Imperfect Repair -- 5. Systems with Multi-components -- 6. Reliability of Semi–Markov Systems -- 7. Hazard Regression Analysis.
In: Springer eBooksSummary: 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.
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1. Lifetime Data -- 2. Models for Perfect Repair -- 3. Models for Minimal Repair -- 4. Models for Imperfect Repair -- 5. Systems with Multi-components -- 6. Reliability of Semi–Markov Systems -- 7. Hazard Regression Analysis.

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.

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