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(R). 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.
General
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