In this paper, we discuss linear inference for the lifetime distribution of components based on a Type-II censored lifetime data of reliability systems with known signatures. We derive the best linear unbiased estimators (BLUE) for the parameter(s) in general scale and location-scale parameter families. The exact computational formulas of the BLUE and their variances and covariance are provided. Selected tables of the coefficients of BLUE are presented for the exponential and extreme value distributions. Using these, best linear unbiased predictors of future system failure times are discussed. Finally, two examples are provided to illustrate all the methods of inference developed here.
We introduce two new families of univariate distributions that we call hyperminimal and hypermaximal distributions. These families have interesting applications in the context of reliability theory in that they contain that of coherent system lifetime distributions. For these families, we obtain distributions, bounds, and moments. We also define the minimal and maximal signatures of a coherent system with exchangeable components which allow us to represent the system distribution as generalized mixtures (i.e., mixtures with possibly negative weights) of series and parallel systems. These results can also be applied to order statistics (k-out-of-n systems). Finally, we give some applications studying coherent systems with different multivariate exponential joint distributions.
Berzelius failed to make use of Faraday's electrochemical laws in his laborious determination of equivalent weights.