Improved Estimation of Weibull Parameters Considering Unreliability Uncertainties

We propose a linear regression method for estimating Weibull parameters from life tests. The method uses stochastic models of the unreliability at each failure instant. As a result, a heteroscedastic regression problem arises that is solved by weighted least squares minimization. The main feature of our method is an innovative s-normalization of the failure data models, to obtain analytic expressions of centers and weights for the regression. The method has been Monte Carlo contrasted with Benard´s approximation, and Maximum Likelihood Estimation; and it has the highest global scores for its robustness, and performance.