Applied Nonparametric Statistics in Reliability

Nonparametric statistics have probably become the more appealing 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, social sciences and physical 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 performance 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; bootstrap-based estimation methods for the rate of occurrence of failures of repairable systems; nonparametric estimation of the kernel-matrix of a semi-Markov process; nonparametric estimation of the structure function of a reliability system; and lifetime regression analysis.
Besides the explanation of the mathematical background, some numerical or simulation methods are presented as illustrative examples where computer-based methods are implemented. 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.