We propose a simple multiple outlier identification method for parametric location-scale and shape-scale models when the number of possible outliers is not specified. The method is based on a result giving asymptotic properties of extreme z-scores. Robust estimators of model parameters are used defining z-scores. An extensive simulation study was done for comparing of the proposed method with existing methods. For the normal family, the method is compared with the well known Davies-Gather, Rosner’s, Hawking’s and Bolshev’s multiple outlier identification methods. The choice of an upper limit for the number of possible outliers in case of Rosner’s test application is discussed. For other families, the proposed method is compared with a method generalizing Gather-Davies method. In most situations, the new method has the highest outlier identification power in terms of masking and swamping values. We also created R package outliersTests for proposed test.

The accelerated failure time (AFT) model is most commonly used model in accelerated life testing (ALT). This model is restrictive as failure time distributions under different constant stresses differ only in terms of scale. If it is not the case, most papers on ALT use a generalization of the AFT model supposing that under different stresses not only scale but also shape parameters are different. This model has an undesirable property for accelerated experiments—the survival functions corresponding to usual and accelerated stresses intersect. In this paper, we propose flexible generalizations of the AFT model. Estimation procedures and properties of estimators are discussed. Goodness-of-fit (GOF) tests for the given models are proposed. Examples of data analysis are provided. Generalization of the results in case of step stresses is presented.