Testing the Performance of the Power Law Process Model Considering the Use of Regression Estimation Approach

Within the class of non-homogeneous Poisson process (NHPP) models and as a result of the simplicity of the mathematical computations of the Power Law Process (PLP) model and the attractive physical explanation of its parameters, this model has found considerable attention in repairable systems literature. In this article, we conduct the investigation of new estimation approach, the regression estimation procedure, on the performance of the parametric PLP model. The regression approach for estimating the unknown parameters of the PLP model through the mean time between failure ( TBF) function is evaluated against the maximum likelihood estimation (MLE) approach. The results from the regression and MLE approaches are compared based on three error evaluation criteria in terms of parameter estimation and its precision, the numerical application shows the effectiveness of the regression estimation approach at enhancing the predictive accuracy of the TBF measure.